Graduate Theses and Dissertations
Graduate College
2011
problems in graph theory and probability
Jihyeok Choi
Iowa State University
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Problems in graph theory and probability
by
Jihyeok Choi
A dissertation submitted to the graduate faculty
in partial fulfillment of the requirements for the degree of
DOCTOR OF PHILOSOPHY
Major: Mathematics
Program of Study Committee:
Maria Axenovich, Co-major Professor
Sunder Sethuraman, Co-major Professor
Ryan Martin
Sung-Yell Song
Eric Weber
Iowa State University
Ames, Iowa
2011
c Jihyeok Choi, 2011. All rights reserved.
Copyright ii
DEDICATION
To my sister in heaven
iii
TABLE OF CONTENTS
LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
v
ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
vi
INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
PART I
Time-dependent preferential attachment models
INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
2
5
CHAPTER 1. Large deviations analysis of time-dependent preferential attachment schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7
1.1
7
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.1.1
Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10
1.1.2
Main Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14
Proof of Theorem 1.1.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
20
1.2.1
Upper bound . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
24
1.2.2
Lower bound . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
27
1.3
Proof of Theorem 1.1.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
37
1.4
Proof of Theorem 1.1.3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
40
1.5
Proof of Theorem 1.1.4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
41
1.2
iv
PART II
Graph coloring
INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
CHAPTER 1. A note on monotonicity of mixed Ramsey numbers . . . . . .
47
48
50
51
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
51
1.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
51
1.2
Definitions and proofs of main results . . . . . . . . . . . . . . . . . . . . . . .
53
BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
58
CHAPTER 2. On colorings avoiding a rainbow cycle and a fixed monochromatic subgraph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
60
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
60
2.1
Introduction and main results . . . . . . . . . . . . . . . . . . . . . . . . . . . .
60
2.2
Definitions and preliminary results . . . . . . . . . . . . . . . . . . . . . . . . .
62
2.3
Proof of Theorem 2.1.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
63
2.4
More precise results for C4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
74
BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
77
CHAPTER 3. A short proof of anti-Ramsey number for cycles . . . . . . . .
79
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
79
3.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
79
3.2
Definitions and proofs of main results . . . . . . . . . . . . . . . . . . . . . . .
80
BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
84
CHAPTER 4. General Conclusions . . . . . . . . . . . . . . . . . . . . . . . . .
4.1
Future improvements and other directions . . . . . . . . . . . . . . . . . . . . .
85
85
v
LIST OF FIGURES
Figure 1.1
Numerical solutions of Euler equations . . . . . . . . . . . . . . . . . .
16
Figure 1.2
Degree distribution for a step function . . . . . . . . . . . . . . . . . .
19
Figure 1.3
Evolution of a degree distribution . . . . . . . . . . . . . . . . . . . . .
20
Figure 2.1
Tails . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
65
Figure 2.2
A rainbow cycle in Claim 1.3 . . . . . . . . . . . . . . . . . . . . . . .
67
Figure 2.3
Representing graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . .
68
Figure 2.4
A rainbow cycle in Claim 2.1 and Claim 2.2-1.(1) . . . . . . . . . . . .
69
Figure 2.5
A rainbow cycle in Claim 2.2-1.(2) . . . . . . . . . . . . . . . . . . . .
69
Figure 2.6
Rainbow cycles in Claim 2.2-1.(3) . . . . . . . . . . . . . . . . . . . . .
70
Figure 2.7
Rainbow cycles in Claim 2.2-2.(1) . . . . . . . . . . . . . . . . . . . . .
70
Figure 2.8
Rainbow cycles in Claim Claim 2.2-2.(2) . . . . . . . . . . . . . . . . .
71
Figure 3.1
A rainbow cycle (v2 , vi , vi−1 , . . . , v3 , vi+1 , vi+2 , . . . , vk+1 , v2 ). . . . . . . .
83
vi
ACKNOWLEDGEMENTS
I am grateful to my advisors, Maria Axenovich and Sunder Sethuraman, for their support
and guidance over the past years. I also thank my committee members, Drs. Ryan Martin,
Eric Weber and Sung-Yell Song for their help, guidance and patience.
vii
INTRODUCTION
In this dissertation, our main object of study is a (hyper) graph, a pair of a set and a
collection of its subsets. Although by definition a graph is purely set theoretic, it has a very
nice representation, when one considers only 2-element subsets, as a diagram consisting of a set
of points, called vertices, together with lines, called edges, joining certain pairs of these points.
Many real-world situations are expressed in terms of graphs in this manner. For example,
for social systems, in the simplest description, we may think of people as vertices where the
(undirected) edge between two people exists if and only if they know each other. Another
example is the world wide web in which the vertices are the web pages available at the website
and a (directed) edge from page A to page B exists if and only if A contains a link to B. Further
the first example may be thought as a (edge) coloring of a graph, namely given n people and
n
2 edges between all possible pairs of them, we label an edge (two people) by a color blue if
they know each other, or by a color red otherwise.
Graph theory finds many applications in other fields including biology, chemistry, computer
science, linguistics, physics and sociology. Further, with connections to other branches of mathematics, many various tools are being employed to considerable effect from algebra, analysis,
geometry, number theory, probability, and topology. In particular in this thesis, we use mainly
two methods, probabilistic and structural, on some problems on graphs.
This dissertation is based on journal papers (published, submitted, or in preparation),
and organized as follows. In Part I, containing the paper “Large deviations analysis of timedependent preferential attachment schemes” in preparation, we study a random graph model
associated with ‘scale-free’ networks, focused on the degree structure, by means of a probabilistic tool. Here a degree of a vertex is the number of vertices connected to the vertex
by an edge. In Part II, with structural methods, we study colorings of graphs in Ramsey and
anti-Ramsey theories. It contains three papers “A note on monotonicity of mixed Ramsey num-
viii
bers” submitted to Discrete Mathematics, “On colorings avoiding a rainbow cycle and a fixed
monochromatic subgraph” published in the Electronic journal of Combinatorics, and “A short
proof of Anti-Ramsey number for cycles” in preparation. Each part has its own introduction
containing more details on problems. The last chapter (Chapter 4) is for general conclusions,
where future research work is presented.
1
PART I
Time-dependent preferential attachment models
2
INTRODUCTION
The study of random graphs, probability distributions on graphs, dates back to the 1950s
(cf. Bollobás and Riordan [3] for an interesting survey). The best known and most studied
is G(n, p) model, in which, with n labeled vertices, edges are presented independently with
probability p from every other edges. The main question on random graphs is that what
asymptotic behaviors the random graphs possess with high probability. For instance, how does
the degree distribution, the fraction of vertices with degree k, denoted by pk , evolve over time?
As each vertex is connected to other vertices with probability p independently, in the G(n, p)
model, the degree distribution has a binomial distribution, which is asymptotically Poisson
distribution with mean np as n → ∞. Therefore the number of vertices with degree k is
decreasing faster than exponentially.
In contrast to the rapid decaying tail of the degree distribution for G(n, p), the recent
discoveries in the field of the network evolution is that a number of large growing networks
including the internet, the world wide web and some social networks, are “scale-free”, that is
the degree distribution of their nodes are of a power-law form. In 1999, Barabási and Albert
[2] proposed a random graph model as a mathematical model to explain this phenomenon. The
basic idea of their model, somewhat generalized version of it, is that, starting from a number of
vertices, each time one adds a new vertex, and connects it to one of the existing vertices with
probabilities proportional to their “weight”. Here, the weight is usually a function, w(k), of
the degree k. Usually the weight function is nondecreasing, which is why this model is called
a preferential attachment graph.
It has been shown that power-law distribution is only for a linear weight function, namely
when w(k) = k + α with constant α > −1, in which case pk ∼ k −(3+α) (Bollobás et al [4], Mori
[8], Athreya et al [1]). For w(k) ∼ k γ , when 0 < γ < 1, pk ∼
µγ
kγ
exp(−ck 1−γ ), which decays
faster than any power but not exponentially fast (Rudas et al [11]). When γ > 1, then there is
3
one vertex with degree of order ∼ n, but all the others have O(1) degrees (Oliveira and Spencer
[9]).
The degree structure of preferential attachment graphs can be captured in urn models,
namely every new connection that a vertex gains can be represented by a new ball added to a
corresponding urn. In particular, Chung, Handjani and Jungreis [6] considered a generalized
Pólya’s urn model such that at each time, with probability p, a new urn with one ball is created;
or with probability 1 − p, a new ball is put in one of existing urns with probability proportional
to a function of its size. They obtained analogous results to preferential attachment graphs.
In particular, for a linear weight function, they proved that the empirical urn size distribution
follows a power law, namely the fraction of urns with k balls is approximately k
1
−1− 1−p
.
Asymptotic behavior of the degree distribution is a consequence of the law of large numbers,
an analysis of typical behavior, by showing the convergence of pk to some constant ck as k → ∞.
Then we may ask how atypical degree distributions occur? In other words,
Question 1
What is the probability that the degree distribution deviates to an atypical
distribution?
The study of the asymptotic behavior of rare events of sequences of probability distributions
is called the large deviation theory. Bryc et al [5] studied large deviations of a class of Markov
chains, which covers the evolution of the vertices with degree 1 in Barabási-Albert model with
a linear weight and the random number of edges added each time. Dereich and Mörters [7] gave
large deviations bound of a general degree distribution for sublinear weights on some version
of Barabási-Albert model.
All the results mentioned above are based on time-homogeneous preferential attachment
models, in which the weight functions do not depend on time. However real-world networks
are time-dependent. Then we may also ask the following question:
Question 2 When the (linear) weight function depends also on time, does the power law
behavior still appear?
A time-dependent urn was introduced in Pemantle [10], where, in a fixed (finite) number
of urns, the number of balls added at each time is dependent of time given by a deterministic
sequence of positive real numbers. Other time-dependent analysis of preferential attachment
4
graphs, in particular for models with time-dependent weight functions, are mostly formal in
the physics literature.
In Chapter 1, we try to answer two questions above. We obtain large deviation bounds for
the empirical degree structure in some time-dependent urn model, when the weight function,
w(k, t) = k + β(t), is linear in ‘degree’ k and β(t) is a function of time t. This model includes
the evolving degree structure of the Barabási-Albert preferential attachment model, and also
the Pólya urn model of Chung-Handjani-Jungreis. For fixed n ≥ 1 (which will be sent to
infinity later on), we consider the paths Xn (t) := n1 hZ0n (bntc), Z1n (bntc), . . .i : 0 ≤ t ≤ 1 ,
where Zkn (j) denotes the number of urns with k balls at time j ≤ n, then prove that, roughly
speaking, the probability that the path Xn (t) deviates to other paths ϕ is, for large enough n,
P Xn (·) ∼ ϕ(·) ∼ exp{−nI(ϕ)},
where I is the rate function, or the cost of achieving a degree structure given by ϕ. As a
consequence of large deviations bound, we obtain the law of large numbers, namely, as n → ∞,
Zk (bntc)
n
→ ζk (t) a.s., where ζ(t) = hζ0 (t), ζ1 (t), . . .i is the solution of a coupled system of ODE’s.
We then show a polynomial decay of ζk (t) in terms of bounds, which shows that time-dependent
models are also scale-free in some sense. However, from analyzing the rate function, we see
that the process can deviate to a variety of distributions, including those with finite support,
with finite cost.
5
Bibliography
[1] Athreya, K. B., Ghosh, A. P. and Sethuraman S. (2008). Growth of preferential
attachment random graphs via continuous-time branching processes. Proc. Indian Acad.
Sci. Math. Sci. 118, 473–494.
[2] Barabási, A.-L. and Albert, R. (1999). Emergence of scaling in random networks.
Science 286, 509–512.
[3] Bollobás, B. and Riordan, O. (2009). Random Graphs and Branching Processes.
Handbook of large-scale random networks, Bolyai Soc. Math. Stud. 18, Springer, Berlin,
15-115.
[4] Bollobás, B., Riordan, O., Spencer, J. and Tusnády, G. (2001). The degree
sequence of a scale-free random graph process. Random Structures Algorithms 18, 279–
290.
[5] Bryc, W., Minda D. and Sethuraman S. (2009). Large deviations for the leaves in
some random trees. Adv. in Appl. Probab. 41, 845–873.
[6] Chung F., Handjani, S. and Jungreis, D. (2003). Generalizations of Polya’s urn
problem. Annals of Combinatorics 7, 141–153.
[7] Dereich, S. and Mörters, P. (2009). Random networks with sublinear preferential
attachment: Degree evolutions. Electron. J. Probab. 14, 1222–1267.
[8] Móri, T. F. (2002). On random trees. Studia Sci. Math. Hungar. 39, 143–155.
[9] Oliveira, R. and Spencer, J. (2005). Connectivity transitions in networks with superlinear preferential attachment. Internet Math. 2, 121–163.
6
[10] Pemantle, R. (1990). A time-dependent version of Pólya’s urn. J. Theoret. Probab. 3,
627–637.
[11] Rudas, A., Tóth, B. and Valkó, B. (2007). Random trees and general branching
processes. Random Struct. Algorithms 31, 186–202.
7
CHAPTER 1.
Large deviations analysis of time-dependent preferential
attachment schemes
Jihyeok Choi and Sunder Sethuraman
abstract
Preferential attachment schemes where the selection mechanism is time-dependent are considered, and an infinite dimensional large deviation principle for the sample path evolution of
the empirical degree distribution is found by Dupuis-Ellis type methods. Interestingly, the rate
function, which can be evaluated, includes a term which accounts for the cost of assigning a
fraction of the total degree to an ‘infinite’ degree component.
As a consequence of the large deviation results, a sample path a.s. law of large numbers for
the degree structure is deduced in terms of a coupled system of ODE’s. In addition, power law
bounds for the limiting degree distribution are identified. However, from analyzing the rate
function, one can see that the process can deviate to a variety of distributions, including those
with finite support, with finite cost.
1.1
Introduction
We study a preferential attachment model of time-dependent Pólya’s urns, where at each
step a new urn is created, and a new ball is added to it or an existing urn with probability
depending on the urn’s size and the time of this addition (see Section 1.1.1 for a precise description). This general class includes the evolving degree structure of the Barabási-Albert
preferential attachment model, and also the Pólya urn model of Chung-Handjani-Jungreis.
8
In Barabási-Albert [4], a preferential attachment scheme was proposed as a model for various
types of complex real-world networks which, starting from an initial (finite) graph, evolve by
“preferential attachment”, that is by attaching new vertices to existing ones with probabilities
proportional to a function of its “weight”. An important property of such networks, when the
weight function is linear, which can be proved is “scale-freeness”, that is the proportions of
vertices with degrees 1, 2, . . ., e.g. the degree distribution of the network, at large times is in
power-law form (see [8], [27]), as opposed to when the distribution decays super-exponentially
as in Erdős-Rényi graphs. That “scale-freeness” can be widely observed in natural and manmade systems, including the internet, the world wide web, citation networks, and some social
networks (see [1], [29], [26], [11], [22] and references therein), is in some sense a main reason
for the popularity of the model.
Since Barabási and Albert’s work, much work has been done on versions of these graphs
to understand their structure. A partial selection of this large literature includes: growth and
location of the maximum degree [28], [3], [16]; form of the degree distribution under non-linear
weight functions [30], [31]; spectral gap and cover time of a random walk on the graph [25],
[13]; width and diameter [24], [7], [15]; graph limits [6], [5].
On the other hand, the degree structure of preferential attachment graphs can be captured
in urn models, namely every new connection that a vertex gains can be represented by a new
ball added to a corresponding urn in a collection of urns. A few years ago, Chung-HandjaniJungreis [10] considered a generalized Pólya’s urn model such that at each time, with probability
p, a new urn with one ball is created, and with probability 1 − p, a new ball is put in one of
existing urns with probability proportional to a function of its size. They proved, among other
results, when the size function is linear, analogous to preferential attachment graphs, that the
empirical urn size distribution is in the form of a power law.
As mentioned in [18], understanding preferential attachment or urn models when the weight
function depends on time allows for more realistic models since real world networks are timedependent. However, work on such time-dependent schemes is mostly formal in the physics
9
literature, e.g. Dorogovtsev-Mendes [17, Section E] where master equations are used to analyze
continuous-time time-dependent models. We remark, on the other hand, in [12] and [3], a law
of large numbers (LLN) for the empirical degree distribution is proved for a type of ‘timedependent’ scheme where the weight function is fixed, but the process adds a random number
of edges at each time.
Given this literature, detailing the large deviation behavior of the empirical degree/size
distribution in time-dependent preferential attachment schemes is a natural problem which
gives much understanding of typical and in particular atypical evolutions. We remark, even in
the usual time-homogeneous models, large deviations of the full degree distribution is an open
question. Previous large deviation work in preferential attachment models have focused on one
dimensional objects, for instance the number of leaves in time-homogeneous processes [9], or
the degree growth of a single vertex with respect to a type of ‘mean-field’ dynamics (where any
vertex may attach to a newly added vertex with a small chance) [16]. See references therein
for other large deviations literature on random trees, and balls-in-bins models.
In this context, our main work on a generalized preferential attachment model includes an
infinite dimensional sample path large deviation principle (LDP) for the joint empirical distribution of the numbers of urns containing 0, 1, 2, . . . balls, that is the ‘empirical degree structure’,
when the initial configuration, not necessarily fixed, satisfies a limit condition (Theorem 1.1.2).
As in many cases, when the object of interest are the numbers of urns with 0, 1, . . . , d balls when
d < ∞, we state corresponding finite-dimensional LDP’s, with respect to explicit rate functions, which allow for instance variational analysis to find optimal trajectories for the sample
path to achieve a given empirical distribution (Theorem 1.1.1). As a consequence of the large
deviations results, we obtain an a.s. sample path LLN for the urn counts in terms of a system
of coupled ODE’s (Theorem 1.1.3). Finally, the LLN limit trajectories are shown to have power
law-type behavior in terms of bounds (Theorem 1.1.4), although it is argued through numerical
studies that the general behavior can interpolate between these bounds (Fig. 1.2).
Interestingly, through calculation with the infinite-dimensional rate function, one can answer the questions: Which degree/size distributions can the process deviate to with finite cost?
10
For instance, naively, one might ask must the finite cost distributions by fully supported on
the non-negative integers? Can some part of the total degree/size be lost in a finite cost distribution? The answers turn out to be NO and YES. Moreover, non-power law distributions can
be achieved with finite rate. See the remarks after Theorem 1.1.2 for more discussion.
We also remark the large deviations and other work are with respect to the process starting
from either ‘small’ or ‘large’ initial configurations, that is when the initial urn collection has
o(n) balls (for instance, finite), or when the size of the collection is on order n respectively.
It appears that such general initial configurations, which enter into all result statements, have
not been considered before.
The main idea for the results is to extend a variational control problem/weak convergence
approach of Dupuis and Ellis (cf. [19]) to establish finite-dimensional LDP’s in the timedependent setting. Then, a projective limit approach, and some analysis to identify the rate
function, is used to obtain the infinite-dimensional LDP. For the LLN and power-law results, a
coupled system of ODE’s, which governs the typical degree distribution evolution, is identified,
and analyzed.
In the next two subsections, we specify more carefully our model and results.
1.1.1
Model
Let p(t) : [0, 1] → [0, 1] and β(t) : [0, 1] → [0, ∞) be given functions. An urn configuration
U = hbx i is a finite list of non-negative integers bx , representing the number of balls in urn x.
For n ≥ 0 and an initial urn configuration U0n , we define a growing sequence {Ujn }0≤j≤n of urn
configurations by the following time-dependent iterative scheme:
• Start at step 0, with the initial urn configuration U0n .
n , we first create a new urn with
• At step j + 1 ≤ n, to form a new urn configuration Uj+1
no ball. Then,
– with probability p(j/n), we place a new ball in that urn;
11
– with probability 1 − p(j/n), we place a new ball in one of other urns with probability
proportional to
bx + β(j/n)
.
y∈U n (by + β(j/n))
P
j
This scheme recovers the degree distributions in the following models.
(1) ‘Classical’ Barabási-Albert processes. When p(t) ≡ 0 and β(t) ≡ 1, an urn with k ≥ 0
balls corresponds to a vertex with degree k + 1 = m ≥ 1.
(2) ‘Offset’ Barabási-Albert processes. When p(t) ≡ 0 and β(t) ≡ β ≥ 0, an urn with k ≥ 0
balls has weight k + β which corresponds to a vertex with degree k + 1 = m ≥ 1 and
weight m + (β − 1) in the BA process with offset β − 1.
(3) Chung-Handjani-Jungreis model of Pólya urns. When p(t) ≡ p and β(t) ≡ 0, the number
of urns of size k ≥ 1 is recovered in the CHJ model. [We note, however, in our model,
the number of “empty” urns, which have no weight when β(t) ≡ 0, is also kept track of.]
Let now bU0n be the total number of balls in U0n and let |U0n | be the number of urns in U0n .
P
Then total number of balls in Ujn with |U0n | + j urns is y∈U n by = bU0n + j. The total weight
j
of the configuration after the j-th step is
snj :=
X
by + β(j/n) = (1 + β(j/n))j + bU0n + β(j/n)|U0n |.
(1.1)
y∈Ujn
n (j)
Let Zin (j) be the number of urns with i balls at time j ≤ n and, for d ≥ 0, let Zd+1
denote the number of urns with more than d balls at time j ≤ n. These quantities satisfy
d+1
X
Zin (j) = |U0n | + j,
i=0
and
d+1
X
iZin (j) ≤ bU0n + j.
(1.2)
i=0
Define now vectors in Rd+2 ,
f0d := h0, 1, 0, . . . , 0i,
fid := h1, 0, . . . , 0, −1, 1, 0 . . . , 0i, where − 1 is at the (i + 1)th position for 1 ≤ i ≤ d
d
fd+1
:= h1, 0, . . . , 0i.
12
For y = hy0 , y1 , . . . , yd+1 i ∈ Rd+2 and 0 ≤ i ≤ d + 1, denote
[y]i :=
i
X
yl .
l=0
Note that
d
0 ≤ [f ]i ≤ 1
d
for 0 ≤ i ≤ d, [f ]d+1 = 1,
and
0≤
d+1
X
(1 − [f d ]i ) ≤ 1.
(1.3)
i=0
Consider now the truncated degree distribution
n
o
n
Zn,d (j) := hZ0n (j), Z1n (j), . . . , Zdn (j), Z̄d+1
(j)i | 0 ≤ j ≤ n ,
n (j) =
where Z̄d+1
n
k≥d+1 Zk (j)
P
= j + bU0n −
Pd
n
k=0 Zk (j),
which forms a discrete time Markov
chain with initial state Zn,d (0) corresponding to the initial urn configuration U0n and one-step
transition property,
Z
n,d
(j + 1) − Z
n,d
(j) =




f0d ,




fid ,






f d ,
d+1
β(j/n)Z0n (j)
, for i = 0
with prob. p(j/n) + 1 − p(j/n)
sn
j
(i+β(j/n))Zin (j)
with prob. 1 − p(j/n)
, for 1 ≤ i ≤ d,
sn
j
Pd
(i+β(j/n))Zin (j)
with prob. 1 − p(j/n) 1 − i=0
.
n
s
j
(1.4)
We also define the full degree distribution {Zn,∞ (j) := hZ0n (j), Z1n (j), . . .i | 0 ≤ j ≤ n}
which is also a Markov chain on R∞ with increments

n,∞


f0∞ , with prob. p(j/n) + 1 − p(j/n) β(j/n)Zn0 (j) , for i = 0
sj
Zn,∞ (j + 1) − Zn,∞ (j) =
n,∞

(j)

i
f ∞ , with prob. 1 − p(j/n) (i+β(j/n))Z
, for i ≥ 1
i
sn
j
(1.5)
where f0∞ = h0, 1, 0, . . . , 0, . . .i and fi∞ = h1, 0, . . . , 0, −1, 1, 0, . . . , 0, . . .i with the ‘−1’ being in
the (i + 1)th place.
We will assume throughout the following initial condition.
(LIM) With respect to constants ci ∈ [0, 1] for i ≥ 0, initially
X
1 n
Zi (0) =: ci , and c̃ :=
ici < ∞.
n→∞ n
lim
i≥0
13
Define also
c :=
X
ci ,
c̄d :=
i≥0
X
ci ,
and cd := hc0 , c1 , . . . , cd , c̄d i.
i≥d+1
We remark one can classify the initial configurations depending on when ci ≡ 0 or when
ci > 0 for some i ≥ 0.
• (Small Configuration) ci ≡ 0 for any i ≥ 0. Here, the initial urn configurations are in a
sense small in that their size is o(n). This is the case when the initial configurations do
not depend on n for instance.
• (Large Configuration) ci > 0 for some i ≥ 0. In this case, the initial state is already a
well-developed configuration whose size is of order n.
The main results will be on the family of stochastic processes {Xn,d (t) | 0 ≤ t ≤ 1} and
{Xn,∞ (t) | 0 ≤ t ≤ 1} obtained by linear interpolation of the discrete-time Markov chains
1 n,d
(j)
nZ
and
1 n,∞
(j)
nZ
Xn,d (t) :=
Xn,∞ (t) :=
respectively. For t ≥ 0, let
1 n,d
nt − bntc n,d
Z (bntc) +
Z (bntc + 1) − Zn,d (bntc) ,
n
n
nt − bntc n,∞
1 n,∞
Z
(bntc) +
Z
(bntc + 1) − Zn,∞ (bntc) .
n
n
The trajectories Xn,d (t) lie in C([0, 1]; Rd+2 ), and are Lipschitz, with constant at most 1,
Q
satisfying Xn,d (0) = n1 Zn,d (0). On the other hand, Xn,∞ (t) ∈ ∞
i=1 C([0, 1]; R), considered
with the weak topology, where Xn,∞ (0) = n1 Zn,∞ (0).
We now specify the assumptions on p(t) and β(t) used for the main results.
(ND) p and β are piecewise continuous and, for some constants 0 ≤ p0 < 1, β0 > 0,
0 ≤ p(·) ≤ p0 and β0 ≤ β(·) < ∞.
(1.6)
We discuss more on (ND) in the remark after Theorem 1.1.1.
We note, throughout the article, we use conventions
Z
0 log 0 = 0 log(0/0) = 0, x/0 = ∞ for x > 0, and E[X; A] =
X dP.
A
(1.7)
14
1.1.2
Main Results
We now recall the statement of a large deviation principle (LDP). A sequence {X n } of
random variables taking values in a complete separable metric space X satisfies the LDP with
rate n and good rate function J : X → [0, ∞] if for each M < ∞, the level set {x ∈ X | J(x) ≤
M } is a compact subset of X , i.e. J has compact level sets, and if the following two conditions
hold.
(i) Large deviation upper bound. For each closed subset F of X
lim sup
n→∞
1
log P {X n ∈ F } ≤ − inf J(x).
x∈F
n
(ii) Large deviation lower bound. For each open subset G of X
lim inf
n→∞
1.1.2.1
1
log P {X n ∈ G} ≥ − inf J(x).
x∈G
n
Empirical degree distribution
For d ≥ 1, we now state the LDP for {Xn,d (t) | 0 ≤ t ≤ 1}. Let Id : C([0, 1]; Rd+2 ) → [0, ∞]
be the function given by
1
Z
(1 − [ϕ̇(t)]0 ) log
Id (ϕ) =
0
+
1 − [ϕ̇(t)]0
β(t)ϕ0 (t)
p(t) + (1 − p(t)) (1+β(t))t+c̃+cβ(t)
d
X
(1 − [ϕ̇(t)]i ) log
1 − [ϕ̇(t)]i
(i+β(t))ϕi (t)
(1 − p(t)) (1+β(t))t+c̃+cβ(t)
P
d
X
1 − di=0 (1 − [ϕ̇(t)]i )
+ 1−
(1 − [ϕ̇(t)]i ) log
Pd
dt,
i=0 (i+β(t))ϕi (t)
(1 − p(t)) 1 − (1+β(t))t+c̃+cβ(t)
i=0
i=1
(1.8)
where ϕ(0) = cd , ϕi ≥ 0 is Lipschitz with constant 1 such that 0 ≤ [ϕ̇]i ≤ 1 for 0 ≤ i ≤ d,
Pd+1
Pd+1
i=0 ϕ̇i (t) = 1,
i=0 iϕ̇i (t) ≤ 1 for almost all t, and the integral converges; otherwise, Id (ϕ) =
∞. It will turn out that Id is convex and is a good rate function.
Theorem 1.1.1 (Sample path LDP). Under assumption (ND), the sequence {Xn,d } of C([0, 1]; Rd+2 )valued random variables satisfies an LDP with rate n and convex, good rate function Id .
Remark 1. We now comment on the assumption (ND).
15
(A) In some sense, it specifies that the process considered is ‘non-degenerate’. (ND) does
not cover some ‘boundary’ cases, for instance, when p(t) ≡ 1, the process is deterministic in
that at each time, one places a new ball in a new urn. Also, when β(t) ≡ 0 and p(t) ≡ 0, urns
without a ball have no weight, and all new balls are placed into urns in the initial configuration.
Although one should still obtain an LDP in these cases, and other boundary cases which are
less ‘degenerate’, the form of the rate function may differ in that some increments may not be
possible.
(B) On the other hand, assumption (ND) is a natural condition with respect to the convergence estimates needed for the proof of the lower bound in the LDP. However, the LDP
upperbound holds without the assumption (ND).
Remark 2. One can recover the LDP at a fixed time, say t = 1, by the contraction principle
with respect to continuous function F : C([0, 1]; Rd+2 ) → Rd+2 defined by F (ϕ) = ϕ(1), so that
F (Xn,d ) = Xn,d (1) = n1 Zn,d (n). Then, Theorem 1.1.1 implies the LDP for
1 n,d
(n)
nZ
with rate
function given by the variational expression
n
o
K(x) = inf Id (ϕ) | ϕ(0) = cd , ϕ(1) = x .
(1.9)
In Figure 1.1, we consider optimal trajectories for the number of empty urns, { n1 Z0n (bntc) : 0 ≤
t ≤ 1}, when d = 0, given that
1 n
n Z0 (n)
= x for various values of x.
We now extend the finite-dimensional LDP results to the infinite dimensional case (d = ∞).
Q
Define for ξ ∈ ∞
i=0 C([0, 1]; R) the function
I ∞ (ξ) =
Z
1
h
˙ 0 ) log
lim (1 − [ξ(t)]
0 d→∞
d
X
˙ i ) log
+
(1 − [ξ(t)]
˙ 0
1 − [ξ(t)]
β(t)ξ0 (t)
p(t) + (1 − p(t)) (1+β(t))t+c̃+cβ(t)
˙ i
1 − [ξ(t)]
(i+β(t))ξi (t)
(1 − p(t)) (1+β(t))t+c̃+cβ(t)
P
d
i
X
˙ i)
1 − di=0 (1 − [ξ(t)]
˙
+ 1−
(1 − [ξ(t)]i ) log
Pd
dt
i=0 (i+β(t))ξi (t)
(1 − p(t)) 1 − (1+β(t))t+c̃+cβ(t)
i=0
i=1
˙ i (t) ≤ 1 for i ≥ 0,
where ξi (0) = ci , ξi (t) ≥ 0 is Lipschitz with constant 1, 0 ≤ [ξ]
P∞ ˙
i=0 ξi (t) = 1
almost all t, and the integral converges; otherwise I ∞ (ξ) = ∞. It will turn out through a
projective limit approach (cf. [14, Section 4.6]) that I ∞ is well-defined, convex and a good rate
16
jHtL
1
0.9
0.8
0.7
0.6
0.5
0.2
Figure 1.1
0.4
0.6
0.8
1
t
The red curves are numerical solutions of the Euler equations with respect
1
to (1.9) for n1 Z0n (n) with p(t) = t, β(t) = 1, Zkn (0) = 2k+1
n for k ≥ 0 when
x = 0.6, 0.7, 0.8, 0.9, 1. The blue curve is the the LLN line for which I0 (ϕ) = 0
function, and the limit in the integrand exists because the expressions in square brackets are
increasing in d.
Theorem 1.1.2 (Sample path LDP - infinite dimension). Given assumption (ND), the sequence
Q
{Xn,∞ } of ∞
i=0 C([0, 1]; R)-valued random variables satisfies an LDP with rate n and convex,
good rate function I ∞ .
Remark 3. From the result, one can see that degree sequences not fully supported on the nonP
negative integers, that is when i≥0 ϕ̇i (·) < 1, cannot be achieved with finite cost. Intuitively,
one can understand this as follows: The #(urns) with size larger than A at time n is bounded
by #(balls at time n)/A, and so the proportion of these urns is on order A−1 , which vanishes
as A ↑ ∞.
On the other hand, it seems some of the total weight can indeed be lost, that is limd↑∞
(i+β(t))ξi (t)
i=0 (1+β(t))t+c̃+cβ(t)
Pd
< 1 with finite rate. The interpretation is that in the growth evolution,
it is possible to put a large number of balls into a few very large urns with finite cost.
To give an example, let ci ≡ 0, β(t) = 1, p(t) ≡ 0, corresponding to the ‘classical’ Barabási˙ i (t) = 1−α−(i+1) ,
Albert model, and consider ξi (t) = t(α−1)/αi+1 for i ≥ 0 and α ≥ 2. Then, [ξ]
17
P∞ ˙
P∞ ˙
−1 ≤ 1. One may compute that the rate
i=0 iξi (t) = (α − 1)
i=1 ξi (t) = 1, and
∞
X
(α − 1)(i + 1)
1
1
I (ξ) = −
log
log 2.
+ 1−
αi+1
2
α−1
∞
i=0
The second term can be thought of as the cost given to ‘increment’ h1, 0, . . . , 0, . . .i corresponding to when the dynamics puts balls into very large sized urns, informally urns with size infinity,
or in other words when new vertices attach to very large hubs.
If now α ↑ ∞, one is computing the cost of mostly evolving according to increment
h1, 0, . . . , 0, . . .i, which is log 2. An evolution which achieves this is the growing ‘star’ configuration where all new vertices connect to the same vertex. When initially, there are only two
vertices with a single edge between them, this configuration has probability 2−n of occurring
at time n.
One also points out, deviations to non-power law degree sequences such that
P
i≥0 iϕ̇i (·)
=
1, when all the weight is on urns with finite size, is possible with finite rate, e.g. when α = 2
above.
We now turn to the LLN behavior of the d + 2-dimensional model which corresponds to the
“zero-cost” trajectory on which the rate function vanishes. Consider the system of ODEs for
ϕd = ϕ, with initial condition ϕ(0) = cd :
β(t)ϕ0 (t)
,
(1 + β(t))t + c̃ + cβ(t)
β(t)ϕ0 (t)
(1 + β(t))ϕ1 (t)
ϕ̇1 (t) = p(t) + (1 − p(t))
− (1 − p(t))
,
(1 + β(t))t + c̃ + cβ(t)
(1 + β(t))t + c̃ + cβ(t)
(i − 1 + β(t))ϕi−1 (t)
(i + β(t))ϕi (t)
− (1 − p(t))
, for 2 ≤ i ≤ d and
ϕ̇i (t) = (1 − p(t))
(1 + β(t))t + c̃ + cβ(t)
(1 + β(t))t + c̃ + cβ(t)
ϕ̇0 (t) = 1 − p(t) − (1 − p(t))
ϕ̇d+1 (t) = 1 −
d
X
ϕ̇i (t).
(1.10)
i=0
For t ∈ [0, 1], define
ζ d (t) = hζ0 (t), ζ1 (t), . . . , ζ̄d+1 (t)i
18
by
ζ0 (t) :=
ζ1 (t) :=
ζi (t) :=
ζ̄d+1 (t) :=
Z t
1
c0 +
(1 − p(s))M0 (s) ds ,
M0 (t)
0
Z t
β(s)ζ0 (s)
1
c1 +
p(s) + (1 − p(s))
M1 (s) ds ,
M1 (t)
(1 + β(s))s + c̃ + cβ(s)
0
Z t
(i − 1 + β(s))ζi−1 (s)
1
(1 − p(s))
ci +
Mi (s) ds , for 2 ≤ i ≤ d, and
Mi (t)
(1 + β(s))s + c̃ + cβ(s)
0
Z
d
t
X
(d + β(s))ζd (s)
t+c−
(1 − p(s))
ds.
(1.11)
ζi (t) = c̄d +
(1 + β(s))s + c̃ + cβ(s)
0
i=0
where
Mi (t) :=

R


exp − 1 (1 − p(u))
t
i+β(u)
(1+β(u))u+c̃+cβ(u)
du ,

R

i+β(u)
exp t (1 − p(u))
0
(1+β(u))u+c̃+cβ(u) du ,
if ci = 0
if ci 6= 0
for 0 ≤ i ≤ d.
Define also the infinite distribution ζ ∞ (t) := hζ0 (t), ζ1 (t), . . .i ∈
Q∞
i=0 C([0, 1]; R).
We now state a LLN for Xn,d , as a consequence of the LDP upper bound.
Theorem 1.1.3 (LLN). Let d ≥ 0 be fixed. We have ζ d is the unique solution to the system
(1.10), with the initial condition ϕ(0) = cd . Also, in the sup topology on C([0, 1]; Rd+2 ),
Xn,d (·) → ζ d (·) a.s.
As a consequence, we have in the product topology that Xn,∞ (·) → ζ ∞ (·).
Here, ζ d , and ζ ∞ are the limiting “urn-size” distributions of the d+2 and infinite-dimensional
processes. We now consider its “scale-freeness.” Although it seems difficult to control each ζi ,
under some assumptions, nevertheless ζ d has “power law” behavior, in terms of bounds on
[ζ d ]i . In general, it appears ζ d can interpolate between the bounds (cf. Figure 1.21 ). Moreover,
analyzing ζ d allows to follow the typical evolution of the continuum graph (cf. Figure 1.3).
Theorem 1.1.4 (Power Law).
Assume 0 ≤ pmin ≤ p(·) ≤ p0 =: pmax < 1, and 0 < β0 =:
βmin ≤ β(·) ≤ βmax < ∞. Then ζ d may be bounded between two power laws:
1. When the initial configuration is small, i.e. ci ≡ 0, we have for 0 ≤ i ≤ d and t ≥ 0 that
[η 0 ]i t ≤ [ζ d (t)]i ≤ [η]i t.
1
As a curiosity, we note a very similar figure is found in [23] with respect to Facebook social network data
19
  
 









Figure 1.2
The thick curve is the (numerical) degree distribution at time t = 1 using the LLN
path with d = 10000, p(t) = 0, β(t) = 8 for t < 1/100, 1 for t ≥ 1/100 and ck = 0.
The lines have slopes −3 and −10. The plot uses log-log scale.
2. When the initial configuration is large, i.e. ci > 0 for some i ≥ 0, we have, for 0 ≤ i ≤ d
and as t ↑ ∞, that
[η 0 ]i (t + o(1)) ≤ [ζ d (t)]i ≤ [η]i (t + o(1)).
Here
ηi0 :=
C0
1+β
1+ 1−pmin
min
(1 + o(1)),
and
ηi :=
i
i
0
and C, C are positive constants depending on p and β.
C
max
1+ 1+β
1−pmax
(1 + o(1)),
The outline of the paper is now as follows. In Sections 1.2 and Section 1.3, we prove the
finite and infinite dimensional LDP’s, Theorems 1.1.1 and 1.1.2. In Section 1.4, we prove the
law of large numbers (Theorem 1.1.3). Finally, in Section 1.5, we discuss power-law behavior
(Theorem 1.1.4).
20
  
  
 




 









  



 









Figure 1.3


  
 





Thick curves are numerical solutions for the degree distribution using the LLN
path with d = 10000, p(t) = 0, β(t) = 1 and ck = (k + 1)−10 for each time
t = 1, 10, 100, 10000. The lines have slopes −3 and −10. All plots use log-log
scale. It shows the distribution is moving from the slope −10 to the slope −3,
which are from the initial condition ck , and the power law exponent from p and β,
respectively.
1.2
Proof of Theorem 1.1.1
We follow the method and notation of Dupuis-Ellis [19]. Some steps are similar to those
in [9] where the “leaves” in a more simplified graph scheme is considered. However, as many
things differ in our model, in the upper bound, and especially the lower bound proof, we present
the full argument.
We now fix 0 ≤ d < ∞ and equip Rd+2 with the L1 -norm denoted by | · |. Recall, from
assumption (LIM),
n,d
n,d
cn,d = (cn,d
) :=
0 , c1 , . . . , c̄
1 n,d
Z (0) → cd .
n
(1.12)
21
Let
A := sup |cn,d − cd |.
n
Let also
cn,d :=
X
cn,d
i ,
and c̃n,d :=
X
icn,d
i .
i≥0
i≥0
Denote also
~ t) := pn (t), βn (t), σn (t) ,
ξ(n,
(1.13)
where
pn (t) := p(bntc/n),
βn (t) := β(bntc/n),
σn (t) :=
1 n
bntc
sbntc = (1 + βn (t))
+ c̃n,d + cn,d βn (t).
n
n
Let
σ(t) := (1 + β(t))t + c̃ + cβ(t),
~
ξ(t)
:=
p(t), β(t), σ(t) .
We note that, as n → ∞, as p(t) and β(t) are piecewise continuous,
~ t) → ξ(t)
~
ξ(n,
for almost all t.
(1.14)
In the following we will drop the superscript d to save on notation. Define
Xnj :=
1 n,d
Z (j).
n
Then, recall Xn0 = cn,d and Xnj+1 = Xnj + n1 vjn (Xnj ), where vjn (x) has a distribution ρξ(n,j/n),x
.
~
Pd+1
Here, for x = (x0 , x1 , x2 , . . . , xd+1 ) ∈ Rd+2 such that xi ≥ 0 for 0 ≤ i ≤ d + 1,
i=0 (i +
βn (t))xi ≤ σn (t), and A ⊂ Rd+2 ,
ρξ(n,t),x
(A) :=
~
d
X
βn (t)x0 (i + βn (t))xi
δf0 (A) +
1 − pn (t)
δfi (A)
σn (t)
σn (t)
i=1
Pd
(i
+
β
(t))x
n
i
+ 1 − pn (t) 1 − i=0
δfd+1 (A).
σn (t)
pn (t) + 1 − pn (t)
22
From (1.3), the paths Xn (t) = Xn,d (t) belong to
Γd,A
n
d+2
:=
ϕ ∈ C([0, 1]; R ) |ϕ(0) − cd | ≤ A, ϕi is Lipschitz with constant 1,
d+1
X
ϕ̇i (t) = 1,
i=0
Here, we equip
d+1
X
o
iϕ̇i (t) ≤ 1, 0 ≤ [ϕ̇]i ≤ 1 for 0 ≤ i ≤ d + 1 .
(1.15)
i=0
C([0, 1]; Rd+2 )
with the supremum norm, i.e. for ϕ ∈ C([0, 1]; Rd+2 ),
||ϕ||∞ := sup |ϕ(t)| = sup
t∈[0,1]
d+1
X
|ϕi (t)|.
t∈[0,1] i=0
For probability measures µ and ν, let R(µ||ν) denote the relative entropy of µ with respect
R
d+2 ) → R be a
to ν, i.e. R(µ||ν) := log( dµ
dν )dµ if µ ν, ∞ otherwise. Let h : C([0, 1]; R
bounded continuous function. Let also
1
W n := − log E{exp[−nh(Xn )]}.
n
To prove Theorem 1.1.1, we need to establish Laplace principle upper and lower bounds
(cf. [19, Section 1.2]), namely upper bound
lim inf W n ≥
n→∞
inf
{Id (ϕ) + h(ϕ)}
inf
{Id (ϕ) + h(ϕ)}.
ϕ∈C([0,1];Rd+2 )
for a good rate function Id , and lower bound
lim sup W n ≤
ϕ∈C([0,1];Rd+2 )
n→∞
Define, for 0 ≤ j ≤ n, that
1
W n (j, {x0 , . . . , xj }) := − log E{exp[−nh(Xn )] | Xn0 = x0 , . . . , Xnj = xj },
n
and
1
W n := W n (0, ∅) = − log E{exp[−nh(Xn )]}.
n
The Dupuis-Ellis method stems from the following discussion. From the Markov property, for
0 ≤ j ≤ n − 1,
e−nW
n (j,{x
0 ,...,xj })
n
= E{e−nh(X ) | Xn0 = x0 , . . . , Xnj = xj }
n
= E{E{e−nh(X ) | Xn0 , . . . , Xnj+1 } | Xn0 = x0 , . . . , Xnj = xj }
n
n
n
n
= E{e−nW (j+1,{X0 ,...,Xj ,Xj+1 }) | Xn0 = x0 , . . . , Xnj = xj }
Z
1
n
=
e−nW (j+1,{x0 ,...,xj ,xj + n y}) ρξ(n,j/n),x
(dy).
~
j
Rd+2
23
Then, by the variational formula for relative entropy (cf. [19, Proposition 1.4.2]), for 0 ≤ j ≤
n − 1,
Z
1
1
n
W n (j, {x0 , . . . , xj }) = − log
e−nW (j+1,{x0 ,...,xj ,xj + n y}) ρξ(n,j/n),x
(dy)
~
j
n
Rd+2
Z
1
1
n
= inf
R(µ||ρξ(n,j/n),x
W (j + 1, {x0 , . . . , xj , xj + y})µ(dy) .
)+
~
j
µ
n
n
Rd+2
We also have a terminal condition W n (n, {x0 , . . . , xn }) = h(x. ), where x. is the linear interpolated path connecting {(j/n, xj )}0≤j≤n .
We may interpret these equations and terminal condition in terms of dynamic programming
and a particular stochastic control problem. Define (i) Lj = (Rd+2 )j , the state space on which
W n (j, ·) is defined; (ii) U = P(Rd+2 ), the space of probability measures on Rd+2 which is the
control space on which the infimum is taken; (iii) for j = 0, . . . , n − 1, “control” vjn (dy) =
vjn (dy|x0 , . . . , xj ) which is a stochastic kernel on Rd+2 given (Rd+2 )j ; (iv) {X̄nj ; 0 ≤ j ≤ n}, the
“controlled” process which is the adapted path satisfying X̄n0 = c and X̄nj+1 = X̄nj + n1 Ȳjn for
0 ≤ j ≤ n − 1, where Ȳjn , conditional on (X̄n0 , . . . , X̄nj ) has distribution vjn (·) (e.g. P̄ {Ȳjn ∈
dy | X̄n0 , . . . , X̄nj } := vjn (dy | X̄n0 , . . . , X̄nj )), and X̄n. is the piecewise linear interpolation of these
controlled random variables; (v) “running costs” Cj (v) =
1
n R(v||ρ)
for v ∈ P(Rd+2 ); and
(vi) “terminal cost” equal to the function h.
Then W n (j, {x0 , . . . , xj }) satisfies a control problem whose solution is for 0 ≤ j ≤ n − 1
(cf. [19, Section 3.2]),
n−1
1X
n
n
V (j, {x0 , . . . , xj }) = inf
Ēj,x0 ,...,xj
R vi (·)||ρξ(n,i/n),
+ h(X̄. ) ,
~
X̄n
i
n
{vin }
n
i=j
where vin (·) = vin (· | X̄n0 , . . . , X̄ni ), and the infimum is taken over all control sequences {vin }.
Here, Ēj,x0 ,...,xj denotes expectation, with respect to the adapted process X̄n. associated to
{vin }, conditioned on X̄n0 = x0 , . . . , X̄nj = xj .
The boundary conditions are V n (n, {x0 , . . . , xn }) = h(x. ) and
n−1
1X
n
n
n
n
V := V (0, ∅) = inf
Ē
R vj (·)||ρξ(n,j/n),
+ h(X̄. ) .
~
X̄n
j
n
{vjn }
(1.16)
j=0
In particular, by [19, Corollary 5.2.1],
1
W n = − log E{exp[−nh(X̄n. )]} = V n .
n
(1.17)
24
1.2.1
Upper bound
To prove the upper bound, it will be helpful to put the controls {vjn } into continuous-time
n .
paths. Let v n (dy|t) := vjn (dy) for t ∈ [j/n, (j + 1)/n), j = 0, . . . , n − 1, and v n (dy|1) := vn−1
Define
Z
n
v (A × B) :=
v n (A|t)dt
B
for Borel A ⊂
Rd+2
and B ⊂ [0, 1]. Also define the piecewise constant path X̃n (t) := X̄nj for
t ∈ [j/n, (j + 1)/n), 0 ≤ j ≤ n − 1, and X̄n (1) = X̄nn−1 . Then
Z 1
n
n
n
n
W = V = inf
Ē
R v (· | t)||ρξ(n,t),
dt + h(X̄ ) .
~
X̃n (t)
n
{vj }
0
Given ρξ,x
is supported on K := {f0 , f1 , . . . , fd+1 }, if {vjn } is not supported on K, then
~
n
d+2 is compact, for each n, there
R(v n ||ρξ,x
~ ) = ∞. Since |V | ≤ ||h||∞ < ∞ and K ⊂ R
is {vjn } supported on K and corresponding v n (dy × dt) = v n (dy | t) × dt such that, for ε > 0,
Z 1
n
n
n
n
W + ε = V + ε ≥ Ē
R v (· | t)||ρξ(n,t),
dt + h(X̄ ) .
(1.18)
~
X̃n (t)
0
Recall that X̄n takes values in Γd,A . Since Γd,A is compact, and {vjn } is tight, by Prokhorov’s
Theorem, given any subsequence of {v n , X̄n }, there is a further subsubsequence, a probability
space (Ω̄, F̄, P̄ ), a stochastic kernel v on K × [0, 1] given Ω̄, and a random variable X̄ mapping
Ω̄ into Γd,A such that the subsubsequence converges in distribution to (v, X̄). In particular,
since X̄n (0) = cn,d → cd as n → ∞, we have X̄ belongs to
Γd := Γd,0 , those functions such that ϕ(0) = cd (cf.(1.15)).
Then, [19, Lemma 3.3.1] shows that v is a subsequential weak limit of v n , and there exists a
stochastic kernel v(dy | t, ω) on K given [0, 1] × Ω̄ such that P̄ -a.s. for ω ∈ Ω̄,
Z
v(A | t, ω)dt.
v(A × B | ω) =
B
Now, the same proof given for [19, Lemma 5.3.5] shows that (v n , X̄n , X̃n ) has a subsequential
weak limit (v, X̄, X̄), where the last coordinate is with respect to D([0, 1] : Rd+2 ), and P̄ -a.s.
for t ∈ [0, 1], and
Z t Z
Z
yv(dy × ds) =
X̄(t) =
Rd+2 ×[0,t]
˙
X̄(t)
=
Z
yv(dy | t).
K
yv(dy | s) ds
0
K
25
By Skorokhod Representation Theorem, we may take that (v n , X̄n , X̃n ) converges to (v, X̄, X̄)
a.s.. In particular, X̄n → X̄ uniformly a.s., and as X̄ is continuous, it follows that also X̃n → X̄
uniformly a.s. (cf. [19, Theorem A.6.5]).
Let λ denote Lebesgue measure on [0, 1] and ρ × λ product measure on K × [0, 1]. Then
[19, Lemma 1.4.3(f)] yields
Z
1
0
R v n (· | t)||ρξ(n,t),
dt = R v n (· | t) × λ(dt)||ρξ(n,t),
× λ(dt) .
~
~
X̃n (t)
X̃n (t)
We now evaluate the limit inferior of W n using formula (1.18), along a subsequence as
above:
lim inf V n + ε ≥ lim inf Ē
n→∞
n→∞
1
Z
0
R v n (· |t )||ρξ(n,t),
dt + h(X̄n )
~
X̃n (t)
n
o
n
= lim inf Ē R v n (· | t) × λ(dt)||ρξ(n,t),
×
λ(dt)
+
h(
X̄
)
n
~
X̃ (t)
n→∞
n
o
≥ Ē R v(· | t) × λ(dt)||ρξ(t),
~ X̄(t) × λ(dt) + h(X̄)
Z 1
= Ē
R v(· |t )||ρξ(t),
~ X̄(t) dt + h(X̄) .
0
In the second inequality, we used Fatou’s lemma with (i) v n (dy|dt) × λ(dt) → v(dy|dt) × λ(dt)
a.s. as v n → v a.s.; (ii) ρξ(n,t),
⇒ ρξ(t),
~
~ X̄(t) as pn (t) → p(t), βn (t) → β(t), σn (t) →
X̃n (t)
σ(t), X̃n (t) → X̄(t) uniformly on [0, 1] a.s., and ρξ(n,t),x
is continuous in its arguments;
~
(iii) lim inf n→∞ R v n (dy|dt) × λ(dt) || ρξ(n,t),
× λ(dt) ≥ R v(dy|dt) × λ(dt) || ρξ(t),
~
~ X̄(t) ×
X̃n (t)
λ(dt) a.s. as R is lower semi-continuous. (iv) h(X̄n ) → h(X̄) a.s. as h is continuous and
X̄n → X̄ uniformly on [0, 1] a.s.
By [19, Lemma 3.3.3(c)],
R v(·|t)||ρξ(t),
~ X̄(t)
~
≥ L ξ(t),
X̄(t),
Z
zv(dz|t) ,
K
where
Z
n
o
~
L(ξ(t), x, y) := sup hθ, yi − log
exphθ, ziρξ(t),x
(dz) θ ∈ Rd+2
~
K
Z
n
o
= inf R(ν(·|t) || ρξ(t),x
)
ν(·|t) ∈ P(K),
zν(dz|t) = y .
~
K
(1.19)
(1.20)
26
We note, in (1.20), the infimum is attained at some ν0 ∈ P(K) as the relative entropy is convex
R
˙
we have
and lower semicontinuous (cf. [19, Lemma 1.4.3(b)]). Since zv(dz|t) = X̄(t),
Z 1
n
˙
~
lim inf V ≥ Ē
L(ξ(t), X̄(t), X̄(t))dt + h(X̄) .
n→∞
0
As X̄ ∈ Γd , we have
lim inf V
n
n→∞
1
Z
≥ inf
ϕ∈Γd
~
L(ξ(t),
ϕ(t), ϕ̇(t))dt + h(ϕ).
0
~
For ϕ ∈ Γd , we can evaluate the minimizer ν0 in the definition of L ξ(t),
ϕ(t), ϕ̇(t) uniquely:
P
P
Recall that [ϕ̇(t)]i := il=0 ϕ̇l (t). Then, as d+1
i=0 fi ν0 (fi |t) = hϕ̇0 (t), . . . , ϕ̇d+1 (t)i, a calculation
gives
ν0 (ϕ̇(t)|t) =
d
X
(1 − [ϕ̇(t)]i )δfi +
i=0
d
X
[ϕ̇(t)]i − d δfd+1 .
(1.21)
i=0
Since ν0 is absolutely continuous with respect to ρξ(t),ϕ(t)
,
~
~
L ξ(t),
ϕ(t), ϕ̇(t) = R(ν0 (ϕ̇(t)|t) || ρξ(t),ϕ(t)
)
~
= (1 − [ϕ̇(t)]0 ) log
+
d
X
1 − [ϕ̇(t)]0
β(t)ϕ0 (t)
p(t) + (1 − p(t)) (1+β(t))t+c̃+cβ(t)
(1 − [ϕ̇(t)]i ) log
1 − [ϕ̇(t)]i
(1.22)
(i+β(t))ϕi (t)
(1 − p(t)) (1+β(t))t+c̃+cβ(t)
P
d
X
1 − di=0 (1 − [ϕ̇(t)]i )
+ 1−
(1 − [ϕ̇(t)]i ) log
Pd
,
i=0 (i+β(t))ϕi (t)
(1 − p(t)) 1 − (1+β(t))t+c̃+cβ(t)
i=0
i=1
interpreted under our conventions listed at the end of the introduction.
Finally, define
Z
Id (ϕ) :=
1
~
L(ξ(t),
ϕ(t), ϕ̇(t)) dt,
(1.23)
0
when ϕ ∈ Γd , and Id (ϕ) = ∞ otherwise. Since L is convex, Id is convex, and also Id has compact
level sets by the proof of [19, Proposition 6.2.4], and so is a good rate function. Hence, the
Laplace principle upper bound holds with respect to Id .
We will need the following result for the proof of the lower bound in the next section.
Principally, assumption (ND) is needed here to show that linear functions have finite rate.
Lemma 1.2.1. Let `(t) = et + cd be a linear function, where e = (e0 , e1 , . . . , ed+1 ) is such that
P
Pd+1
ei > 0 for i ≥ 0, d+1
i=0 ei = 1, and
i=0 iei ≤ 1. Then, under (ND), Id (`(t)) < ∞.
27
Proof. Noting
Pd
i=0 (1
Z
− [e]i ) =
Pd+1
i=0
1
(1 − [e]0 ) log
Id (`(t)) =
iei ≤ 1, explicitly
0
+
d
X
1 − [e]0
β(t)(e0 t+c0 )
p(t) + (1 − p(t)) (1+β(t))t+c̃+cβ(t)
1 − [e]i log
1 − [e]i
(i+β(t))(ei t+ci )
(1 − p(t)) (1+β(t))t+c̃+cβ(t)
P
d
X
1 − di=0 (1 − [e]i )
+ 1−
(1 − [e]i ) log
dt
Pd
i=0 (i+β(t))(ei t+ci )
(1 − p(t)) 1 − (1+β(t))t+c̃+cβ(t)
i=0
i=1
is bounded under assumption (ND).
1.2.2
Lower bound
Fix a bounded, continuous function h : C([0, 1]; Rd+2 ) → R, and ϕ∗ ∈ Γd such that Id (ϕ∗ ) <
∞. To show the lower bound, it suffices to prove, for each ε > 0, that
lim sup V n ≤ I(ϕ∗ ) + h(ϕ∗ ) + 8ε.
(1.24)
n→∞
The main idea of the argument is to construct from ϕ∗ a sequence of control measures suitable
to evaluate formulas for V n .
Note in the following, to make some expressions simpler, we often take cd+1 := c̄d .
Step 1. Convex combination and Regularization. Rather than work directly with ϕ∗ ,
we consider a convex combination of paths with better regularity: For 0 ≤ θ ≤ 1, let
ϕθ (t) = (1 − θ)ϕ∗ (t) + θ`(t),
(1.25)
where `(t) = et + cd is a linear function such that e satisfies the assumptions of Lemma 1.2.1,
1
1
say e = ( 12 , 212 , . . . , 2d+1
, 2d+1
).
Lemma 1.2.2. As θ ↓ 0, we have
|Id (ϕθ ) − Id (ϕ∗ )| → 0, and |h(ϕθ ) − h(ϕ∗ )| → 0.
28
Proof. By convexity of Id , and finiteness of Id (`(t)) from Lemma 1.2.1,
Id (ϕθ ) ≤ (1 − θ)Id (ϕ∗ ) + θId (`).
On the other hand, since kϕθ − ϕ∗ k∞ < |2θ1| = 2θ(d + 2) ↓ 0, by lower semi-continuity of Id ,
we have
lim inf Id (ϕθ ) ≥ Id (ϕ∗ ).
θ↓0
Also, as h is continuous, we have that |h(ϕθ ) − h(ϕ∗ )| → 0.
Now, fix θ > 0 such that
Id (ϕθ ) ≤ Id (ϕ∗ ) + ε
and
h(ϕθ ) ≤ h(ϕ∗ ) + ε.
Next, for κ ∈ N and t ∈ [0, 1], define
t
Z
γκ (s) ds + cd ,
ψκ (t) =
(1.26)
0
where
Z
(i+1)/κ
γκ (t) = κ
ϕ˙θ (s) ds
i/κ
for t ∈ [i/κ, (i + 1)/κ), 0 ≤ i ≤ κ − 1, and γκ (1) = γκ (1 − 1/κ). Note that ψκ ∈ Γd , and on
[i/κ, (i + 1)/κ) for 0 ≤ i ≤ κ − 1, ψ̇κ (t) equals the constant vector γκ (i/κ). In particular, ψ̇κ is
a step function.
Lemma 1.2.3. For 0 ≤ i ≤ d + 1,
ψκ,i (t) ≥ θ(ei t + ci ),
(1.27)
(1 − [ψ̇κ (t)]i ) ≤ 1 − θed+1 .
(1.28)
d
X
i=0
Proof. These are properties of ϕθ inherited from properties of ϕ∗ ∈ Γd and `, which are preserved with respect to (1.26). Indeed, for each 0 ≤ i ≤ d + 1,
ψκ,i (t) = ϕθ,i (btκc/κ) + (tκ − btκc) ϕθ,i (btκc + 1)/κ − ϕθ,i (btκc/κ)
≥ θ(ei t + ci ).
29
Lastly, (1.28) follows as, noting that
d
X
(1 − [ψ̇κ (t)]i ) =
Pd
i=0 (1
(1 − θ)
i=0
− [e]i ) =
d
X
Pd+1
i=0
iei = 1 − ed+1 ,
(1 − [ϕ̇∗ (t)]i ) + θ
i=0
≤
1−θ + θ
d
X
˙ i ),
(1 − [`(t)]
i=0
d
X
(1 − [e]i ) = 1 − θed+1 .
i=0
Lemma 1.2.4. For large enough κ, we have
h(ψκ ) ≤ h(ϕ∗ ) + 2ε, and I(ψκ ) ≤ Id (ϕ∗ ) + 2ε.
(1.29)
Proof. Since
lim sup |ϕθ (t) − ψκ (t)| = 0,
κ→∞ t∈[0,1]
the inequality with respect to h follows from continuity of h and choosing κ in terms of θ. We
also note that a.s. in t,
Z
(btκc+1)/κ
ϕ˙θ (s) ds → ϕ˙θ (t) as κ ↑ ∞.
ψ̇κ (t) = γκ (t) = κ
btκc/κ
Then, by the form of L (cf. (1.23)), bounds in Lemma 1.2.3, and assumption (ND), we
~
~
have, as κ ↑ ∞, that L(ξ(t),
ψκ (t), ψ̇κ (t)) → L(ξ(t),
ϕθ (t), ϕ̇θ (t)) for almost all t ∈ [0, 1].
~
Also, we can dominate L(ξ(t),
ψκ (t), ψ̇κ (t)) as follows: First bound, using x log x ≤ 0 for
0 ≤ x ≤ 1, that
~
L(ξ(t),
ψκ (t), ψ̇κ (t))
≤ (1 − [ψ˙κ (t)]0 ) log p(t) + (1 − p(t))
−
d
X
(1 − [ψ˙κ (t)]i ) log (1 − p(t))
β(t)ψκ,0 (t)
(1 + β(t))t + c̃ + cβ(t)
(i + β(t))ψ (t)
κ,i
(1 + β(t))t + c̃ + cβ(t)
i=1
Pd
d
X
i=0 (i + β(t))ψκ,i (t)
˙
− 1−
(1 − [ψκ (t)]i ) log (1 − p(t)) 1 −
.
(1 + β(t))t + c̃ + cβ(t)
i=0
Now, as 0 ≤ [ψ̇κ ]i ≤ 1 and 0 ≤
Pd
i=0 (1
− [ψ˙κ ]i ) ≤ 1, we have the further upperbound, using
30
(1.27),
− log p(t) + (1 − p(t))
−
d
X
log (1 − p(t))
i=1
β(t)θ(e0 t + c0 )
(1 + β(t))t + c̃ + cβ(t)
(i + β(t))θ(e t + c ) i
i
(1 + β(t))t + c̃ + cβ(t)
(d + 1 + β(t))θ(ed+1 t + c̄d )
− log (1 − p(t))
(1 + β(t))t + c̃ + cβ(t)
d
X
θ(ei t + ci ) i + β(t)
≤ −
·
log (1 − p(t))
1 + β(t) t + max{c̃, c}
i=0
d + 1 + β(t) θ(ed+1 t + c̄d ) − log (1 − p(t))
·
,
1 + β(t)
t + max{c̃, c}
which is integrable on [0, 1] under assumption (ND).
By dominated convergence, we obtain limκ I(ψκ ) = I(ϕθ ), and therefore the other inequality
with respect to Id .
Let now κ be such that (1.29) holds. Finally, we modify ψκ on the interval [0, δ], for a small
enough δ > 0 to be chosen later.
P
Define ti := δ − dl=i δ + [cd ]l − [ψκ (δ)]l for 0 ≤ i ≤ d, and td+1 := δ; set also t−1 := 0.
Let also
ψ ∗ (t) =
Z
t
γ ∗ (s) ds + cd
(1.30)
0
where
γ ∗ (t) =




fd+1 ,




when 0 ≤ t < t0 ,
fi ,
when ti ≤ t < ti+1 , 0 ≤ i ≤ d,






γκ (t), when t ≥ δ.
Note that γ ∗ may not be defined at some endpoints as possibly ti = ti+1 for some i.
By inspection, ψ ∗ ∈ Γd , and ψ0∗ (t) = t + c0 when 0 ≤ t ≤ t0 . Also, ψ̇ ∗ (t) = fd+1 when
0 ≤ t < t0 and ψ̇ ∗ (t) = fi when ti ≤ t < ti+1 for 0 ≤ i ≤ d. Moreover, we have the following
properties.
31
Lemma 1.2.5. We have ψ ∗ (δ) = ψκ (δ) and t0 ≥ θed+1 δ. Also,
ψ0∗ (t) = t + c0 and ψj∗ (t) = cj for 1 ≤ j ≤ d + 1
when 0 ≤ t < t0 ,
ψ0∗ (t) ≥ θed+1 δ + c0
when t0 < t < t1 ,
ψi∗ (t) ≥ θ(ei δ + ci )
when ti < t < ti+1 and 1 ≤ i ≤ d,
Proof. The lower bound for t0 follows from the integration of both sides in (1.28) and the
definition of t0 . Now, we note that ψ̇0∗ (t) = 0 if t0 ≤ t ≤ t1 , and 1 otherwise. Also, note that
for 1 ≤ i ≤ d, ψ̇i∗ (t) = 1 if ti−1 < t < ti , ψ̇i∗ (t) = −1 if ti < t < ti+1 , and ψ̇i∗ (t) = 0 otherwise.
Thus
ψ0∗ (δ)
Z
=
δ
γ0∗ (s) ds + c0 = δ − (t1 − t0 ) + c0 = ψκ,0 (δ),
0
and for 1 ≤ i ≤ d,
ψi∗ (δ)
Z
=
δ
γi∗ (s) ds + ci = (ti − ti−1 ) − (ti+1 − ti ) + ci = ψκ,i (δ),
0
which proves that ψ ∗ (δ) = ψκ (δ). Since ψ0∗ (t) is nondecreasing, for t ≥ t0 , ψ0∗ (t) ≥ ψ0∗ (t0 ) =
t0 + c0 ≥ θed+1 δ + ci . For 1 ≤ i ≤ d, for ti < t < ti+1 , ψi∗ (t) decreases to its final value
ψκ,i (δ) ≥ θ(ei δ + ci ) by (1.27).
Step 2. More properties of ψ ∗ . We now show the rate of ψ ∗ up to time δ does not
contribute too much.
Lemma 1.2.6. For small enough δ > 0,
Z
δ
~
L(ξ(t),
ψ ∗ (t), ψ̇ ∗ (t)) dt ≤ ε and kψ ∗ − ψκ k∞ < ε.
0
In particular, h(ψ ∗ ) < h(ϕ∗ ) + 3ε and Id (ψ ∗ ) < Id (ϕ∗ ) + 3ε.
32
Proof. Write, for 0 ≤ t ≤ δ,
d
X
∗
∗
~
L(ξ(t), ψ (t), ψ̇ (t)) = R δfd+1 ||ρξ(t),ψ
R δfi ||ρξ(t),ψ
∗ (t) 1(0 < t < t0 ) +
∗ (t) 1(ti < t < ti+1 )
~
~
i=0
Pd
∗
l=0 (l + β(t))ψl (t)
1(0 < t < t0 )
= − log (1 − p(t)) 1 −
(1 + β(t))t + c̃ + cβ(t)
β(t)ψ0∗ (t)
− log p(t) + (1 − p(t))
1(t0 < t < t1 )
(1 + β(t))t + c̃ + cβ(t)
d
X
(i + β(t))ψi∗ (t)
−
1(ti < t < ti+1 ).
log (1 − p(t))
(1 + β(t))t + c̃ + cβ(t)
i=1
By Lemma 1.2.5 and the assumption (ND), this expression is integrable for 0 ≤ t ≤ δ. [It would
be bounded unless c̄d = 0 and c 6= 0, in which case the first term in the expression involves
− log t.] Hence, the first statement follows for small enough δ > 0. Also, the second statement
holds as kψ ∗ − ψκ k∞ = sup0≤t<δ |ψ ∗ − ψκ | ≤ 2δ(d + 2). The last statement is a consequence
now of (1.29).
We will take δ > 0 small enough so that the bounds in the above lemma hold.
Lemma 1.2.7. We have
j−1
1X ∗
∗
lim sup ψ (j/n) −
ψ̇ (l/n) − cd = 0.
n→∞ 0≤j≤n
n
(1.31)
l=0
Also, for j ≥ bδnc and 0 ≤ i ≤ d + 1,
j−1
1X ∗
θ ei j
ψ̇i (l/n) + ci ≥
+ ci ,
n
2 n
(1.32)
l=0
Proof. Since ψ̇ ∗ is piecewise constant, |ψ̇ ∗ (s)− ψ̇ ∗ (l/n)| =
6 0 for at most κ subintervals (cf.(1.26)
and (1.30)), and is also bounded by |2 · 1| = 2(d + 2). Hence,
j−1
j−1 Z (l+1)/n X
1X ∗
∗
d
∗
∗
ψ
(j/n)
−
ψ̇
(l/n)
−
c
=
ψ̇
(s)
−
ψ̇
(l/n)
ds
n
l/n
l=0
l=0
≤
The last statement follows from (1.27).
2(d + 2)
κ.
n
33
Step 3. Admissible control measures and convergence. We now build a sequence of
controls based on ψ ∗ . Define ν0 = ν0 (ψ̇ ∗ (j/n)|j/n) using (1.21). Define


 ν0 (ψ̇ ∗ (j/n)|j/n) when 0 ≤ j ≤ bδnc



n
vj (dy; x0 , . . . , xj ) =
or when j ≥ dδne and xj,i ≥ 4θ (ei δ + ci ) for 0 ≤ i ≤ d + 1,




 ρ
otherwise.
~
ξ(j/n),x
j
Define also X̄n0 = cd , and X̄nj+1 = X̄nj + n1 Ȳjn for j ≥ 0 where
P̄ (Ȳjn ∈ dy|X̄n0 , . . . , X̄nj ) = vjn (dy; X̄n0 , . . . , X̄nj ).
P
n
d
n
Thus, for j ≥ 0, X̄nj = n1 j−1
l=0 Ȳl + c . In particular, for 0 ≤ j ≤ bδnc, X̄j is deterministic
Pj−1 ∗
and X̄nj = n1 l=0
ψ̇ (l/n) + cd .
Define now, for each n ≥ 1, that
j−1
Mnj :=
1X
Ȳln − Ē Ȳln |X̄nl
n
l=0
j−1
=
X̄nj
1X
−
Ē Ȳln |X̄nl − cd
n
(1.33)
l=0
is a martingale sequence for 0 ≤ j ≤ n. Let
n
o
θ
τn := n ∧ min dδne ≤ l ≤ n : X̄nl,i < (ei δ + ci ) for some i .
4
Then, τn is a stopping time and the corresponding stopped process {Mnj∧τn } is also a martingale
for 0 ≤ j ≤ n.
Let now
An :=
n
θed+1 o
sup Mnj∧τn >
.
4n1/8
0≤j≤n
Lemma 1.2.8. For n ≥ δ −8 , on the set Acn , we have τn = n.
Proof. For j ≥ dδne, from the definition of {vjn } and τn , and (1.32), we have
X̄nj∧τn ,i
j∧τn −1
θed+1
1 X
≥ ci +
E Ȳln |X̄nl − 1/8
n
4n
l=0
j∧τn −1
θe
1 X ˙∗
θed+1
θ ei (j ∧ τn )
θ
d+1
= ci +
ψ (l/n) − 1/8 ≥
+ ci − 1/8 ≥ (ei δ + ci ).
n
2
n
4
4n
4n
l=0
Hence, τn = n.
34
We now observe, by Doob’s martingale inequality and bounds, in terms of constants C = Cd ,
that
4
P̄ [An ] ≤ Cn1/2 Ē Mnj∧τn n −1
j∧τ
X
4
n
n
n
−7/2 Ȳl − Ē Ȳl |X̄l = Cn
Ē l=0
−7/2 2
≤ Cn
n
= Cn−3/2 .
(1.34)
We now state the following almost sure convergence.
Lemma 1.2.9.
j−1
n 1 X ˙∗
d
lim sup X̄j −
ψ (l/n) − c = 0
n↑∞ 0≤j≤n
n
a.s.
(1.35)
l=0
Proof. First, by (1.34) and Borel-Cantelli lemma, P̄ (lim sup An ) = 0. On the other hand, on
the full measure set ∪n≥1 ∩k≥n Ack , since τn = n on Acn by Lemma 1.2.8, the desired convergence
holds.
Step 4. We now argue the lower bound through representation (1.16). The sum in (1.16)
equals
Ē
h 1 n−1
X
n
j=0
R(vjn ||ρξ(j/n),
)
~
X̄n
j
i
= Ē
h 1 bδnc
X
n
+ Ē
i
R(vjn ||ρξ(j/n),
)
n
~
X̄
j
j=0
h 1 n−1
X
n
+ Ē
R(vjn ||ρξ(j/n),
); An
~
X̄n
i
j
j=dδne
h 1 n−1
X
n
R(vjn ||ρξ(j/n),
); Acn
~
X̄n
i
j
j=dδne
= A1 + A2 + A3 .
(1.36)
Step 4.1 We first treat the second term A2 in (1.36). Recall, for j ≥ 0, that
σ(j/n) = (1 + β(j/n))
j
+ c̃ + cβ(j/n).
n
(1.37)
35
For j ≥ dδne,
R vjn ||ρξ(j/n),
~
X̄n
j
1 X̄nj,i ≥ (θ/4)(ei δ + ci ) for 0 ≤ i ≤ d + 1 .
= R ν0 (ψ˙∗ (j/n))||ρξ(j/n),
~
X̄n,d
j
Noting (1.23), this is bounded above, using x log x ≤ 0 for 0 ≤ x ≤ 1, by
"
h j i β(j/n)X̄nj,0 − 1 − ψ̇ ∗
log p(j/n) + 1 − p(j/n)
n 0
σ(j/n)
d X
h j i (i + β(j/n))X̄nj,i log 1 − p(j/n)
1 − ψ̇ ∗
n i
σ(j/n)
i=1
#
Pd
d h
n X
j i
(i
+
β(j/n))
X̄
j,i
i=0
− d log 1 − p(j/n) 1 −
−
ψ̇ ∗
n i
σ(j/n)
i=0
×1 X̄nj,i ≥ (θ/4)(ei δ + ci ) for 0 ≤ i ≤ d + 1
−
Further, given assumption (ND), as 0 ≤ [ψ̇ ∗ ]i ≤ 1, d ≤
Pd
i=1 [ψ̇
∗]
i
≤ d + 1 and
d
X
(i + β(j/n))X̄nj,i ≤ σ(j/n) − d + 1 + β(j/n) X̄nj,d+1
i=0
≤ σ(j/n) − d + 1 + β(j/n) · (θ/4)(ed+1 δ + cd+1 ),
this expression is bounded by a constant Cd . Thus, we obtain, for large n,
A2 =
≤
Ē
h 1 n−1
X
n
R(vjn ||ρξ(j/n),
); An
~
X̄n
i
j
j=dδne
Cd · P̄
h
sup |Mnj∧τn | >
0≤j≤n
θed+1 i
< ε.
4n1/8
Step 4.2. Now, for the first term A1 in (1.36), we recall for j ≤ bδnc that X̄nj =
(1.38)
1
n
Pj−1
l=0
ψ̇ ∗ (l/n)+
cd is deterministic. Also note, for 0 ≤ i ≤ d, that ψ˙∗ (t) = fi on ti < t < ti+1 , and ψ˙∗ (t) = fd+1
on 0 = t−1 ≤ t ≤ t0 (cf. near Lemma 1.2.5). Thus, for 0 ≤ j ≤ bδnc, denoting f−1 = fd+1 , we
36
may write, as in the proof of Lemma 1.2.6,
j−1
l
j 1 X
j ~
ψ̇ ∗
= L ξ
,
+ cd , ψ˙∗
j
n n
n
n
l=0
P
P
j−1
d
1
∗
d m=0 ψ̇l (m/n) + c
l=0 (l + β(j/n)) n
= − log (1 − p(j/n)) 1 −
1 0 < j < bt0 nc
σ(j/n)
P
j−1
1
β(j/n) n m=0 ψ̇0∗ (m/n) + cd − log p(j/n) + (1 − p(j/n))
1 bt0 nc < j < bt1 nc
σ(j/n)
P
d
∗
d X
(i + β(j/n)) n1 j−1
m=0 ψ̇i (m/n) + c
1 bti nc < j < bti+1 nc .
−
log (1 − p(j/n))
σ(j/n)
R(vjn ||ρξ(j/n),
)
~
X̄n
i=1
This expression is summable for 0 ≤ j ≤ bδnc – the only possible unbounded term is of the
Rδ
Pbδnc
form −(1/n) j=1 log(j/n) ≤ 0 log(t)dt. [Again, the expression is bounded unless c̄d = 0 and
c 6= 0.] Hence,
A1 = Ē
h 1 bδnc
X
n
i
R(vjn ||ρξ(j/n),
)
< (δ),
n
~
X̄
(1.39)
j
j=0
where (δ) → 0 as δ → 0.
Step 4.3. We now estimate the last term A3 in (1.36). For n ≥ δ −8 , by Lemma 1.2.8,
A3 ≤ Ē
= Ē
h 1 n−1
X
n
i
c
R(vjn ||ρξ(j/n),
)
;
A
∩
{τ
=
n}
n
~
n
X̄n
j
j=dδne
h 1 n−1
X
j j i
L ξ~
, X̄nj , ψ˙∗
; Acn ∩ {τn = n}
n
n
n
j=dδne
bntc i
h Z 1 bntc , X̄nbntc , ψ˙∗
; Acn ∩ Bn
≤ Ē
L ξ~
n
n
δ
where Bn = {X̄nj,i ≥ (θ/4)(ei δ + ci ) for 0 ≤ i ≤ d + 1, j ≥ dδne}. On the event Acn ∩ Bn ,
Ē(Ȳln |X̄nl ) = ψ̇ ∗ (l/n) for l ≥ 0, and so X̄nbntc − ψ ∗ bntc/n → 0 from (1.31).
Also, from the form of L (1.23), and bounds and piecewise continuity of p and β given in
assumption (ND), on this event, L is dominated by a constant Cd as in Step 4.1, and converges
~
to L ξ(t),
ψ ∗ (t), ψ˙∗ (t) for almost all t. Hence, by bounded convergence theorem,
Z
lim sup A3 ≤
n→∞
δ
1
~
L ξ(t),
ψ ∗ (t), ψ̇ ∗ (t) dt.
(1.40)
37
Step 5. Finally, by (1.31) and (1.35), in the sup topology, limn→∞ h(X̄n· ) = h(ψ ∗ (·)).
We now combine all bounds to conclude the proof of (1.24). By (1.16), bounds (1.39),(1.38),
(1.40), and nonnegativity of L, we have
lim sup V n ≤ lim sup Ē
n→∞
n→∞
Z
≤
1
2ε +
h 1 n−1
X
n
i
n
R(vjn ||ρξ(n,j/n),
)
+
h(
X̄
)
~
·
X̄n
j=0
j
~
L ξ(t),
ψ ∗ (t), ψ̇ ∗ (t) dt + h(ψ ∗ ).
0
Then, by Lemma 1.2.6, we obtain (1.24).
1.3
Proof of Theorem 1.1.2
The proof of Theorem 1.1.2 follows from the following two propositions, and is given below.
We first recall the projective limit approach, following notation in [14, Section 4.6]. Let J =
N, Yj = C([0, 1]; Rd+2 ), and define, for 0 ≤ i ≤ j, pij : C([0, 1]; Rj+2 ) → C([0, 1]; Ri+2 )
Q
P
Y ⊂ i≥0 Yi as the subset of
by hϕ0 , . . . , ϕj+1 i 7→ hϕ0 , . . . , ϕi , j+1
l=i+1 ϕl i. Also define lim
←− j
elements x = hx0 , x1 , . . .i such that pij xj = xi , equipped with the product topology. Let also
pj : lim Yj → Yj be the canonical projection, pj x = xj .
←−
Since Id are convex, good rate functions on C([0, 1], Rd+2 ), by the LDP’s Theorem 1.1.1
and [14, Theorem 4.6.1], we obtain the following proposition. Recall the notation in Theorem
1.1.1. For n ≥ 1, let X n,∞ = hXn,0 , Xn,1 , . . . , Xn,d , . . .i.
Proposition 1.3.1. Under assumption (ND), the sequence {X n,∞ } ⊂ lim Yj satisfies an LDP
←−
with rate n and convex, good rate function J ∞ ,
J ∞ (ϕ) = sup{Id (pd (ϕ))}.
d
To establish Theorem 1.1.2, it remains to identify more J ∞ , which is done in the next
proposition. Recall Γd ⊂ C([0, 1]; Rd+2 ) are those elements ϕ = hϕ0 , . . . , ϕd , ϕd+1 i such that
38
ϕ(0) = cd , each ϕi ≥ 0 is Lipschitz with constant 1 such that 0 ≤ [ϕ̇(t)]i ≤ 1 for 0 ≤ i ≤ d,
Pd+1
Pd+1
i=0 iϕ̇i (t) ≤ 1 for almost all t.
i=0 ϕ̇i (t) = 1, and
Let also Γ∗ ⊂ lim Yj be those elements ϕ = hϕ0 , ϕ1 , . . .i such that
←−
ϕd ∈ Γd for d ≥ 0, and limd↑∞ ϕ̇dd+1 (t) = 0 (or limd↑∞ [ϕ̇d (t)]d = 1) for almost all t.
Define
1 − [ϕ̇d (t)]0
Ld (pd (ϕ(t))) = (1 − [ϕ̇d (t)]0 ) log
β(t)ϕd (t)
0
p(t) + (1 − p(t)) (1+β(t))t+c̃+cβ(t)
+
d
X
1 − [ϕ̇d (t)]i
(1 − [ϕ̇d (t)]i ) log
(i+β(t))ϕd (t)
i
(1 − p(t)) (1+β(t))t+c̃+cβ(t)
P
d
X
1 − di=0 (1 − [ϕ̇d (t)]i )
d
+ 1−
(1 − [ϕ̇ (t)]i ) log
Pd
dt.
d
i=0 (i+β(t))ϕi (t)
(1 − p(t)) 1 − (1+β(t))t+c̃+cβ(t)
i=0
i=1
Proposition 1.3.2. Under assumption (ND), the rate function J ∞ (ϕ) diverges when ϕ 6∈ Γ∗ .
However, for ϕ ∈ Γ∗ and almost all t, limd↑∞ Ld pd (ϕ(t)) exists, and we can evaluate
J ∞ (ϕ) =
Z
1
lim Ld pd (ϕ(t)) dt.
0 d↑∞
Q
Q
Proof. First, J ∞ (ϕ) diverges unless ϕ ∈
d≥0 Γd . However, for ϕ ∈
d≥0 Γd such that
P
P
d
d
d
J ∞ (ϕ) < ∞, since supd d+1
i=0 1 − [ϕ̇ (t)]i =
i=0 iϕ̇i (t) ≤ 1 almost all t, we must have limd↑∞
P
d
d
∗
limd↑∞ d+1
i=0 iϕ̇i (t) < ∞. Hence, limd↑∞ 1 − [ϕ̇ (t)]d = 0 almost all t, and ϕ ∈ Γ .
Now, for ϕ ∈ Γ∗ and almost all t, we argue
Lr (pr (ϕ(t))) ≤ Ls (ps (ϕ(t)))
when r < s.
It will be enough to show from the form of the rates, the following:
!
P
r
X
1 − ri=0 (1 − [ϕ̇s (t)]i )
s
Pr
1−
(1 − [ϕ̇ (t)]i ) log
s
i=0 (i+β(t))ϕi (t)
(1
−
p(t))
1
−
i=0
(1+β(t))t+c̃+cβ(t)
≤
s
X
(1 − [ϕ̇s (t)]i ) log
i=r+1
+
1 − [ϕ̇s (t)]i
(i+β(t))ϕs (t)
i
(1 − p(t)) (1+β(t))t+c̃+cβ(t)
!
Ps
s
s (t)]
X
1
−
1
−
[
ϕ̇
i
i=0
.
Ps
1−
(1 − [ϕ̇s (t)]i log
s
i=0 (i+β(t))ϕi (t)
(1
−
p(t))
1
−
i=0
(1+β(t))t+c̃+cβ(t)
(1.41)
39
Consider now h(x) = x log x which is convex for x ≥ 0. Under conventions (1.7), for
non-negative numbers, ai and bi , we have
Pq
Pq
i=p ai
i=p ai
Pq
log Pq
i=p bi
i=p bi

a
i
Pq
= h
= h
b
b
i
i
i=p
i=p
Pq
q
X
b
ai
i=p ai log(ai /bi )
Pq i h
Pq
≤
=
.
bi
i=p bi
i=p bi
Pq
ai
Pi=p
q
i=p bi
!

q
X
bi
i=p
We now finish the proof of (1.41) by applying the last sequence, with p = r + 1 and q = s + 1,
to
aj =
bj



1 − [ϕ̇s (t)]j
for r + 1 ≤ j ≤ s
and

s (t)]
 1−
1
−
[
ϕ̇
for
j
=
s
+
1
i
i=0

(j+β(t))ϕsj (t)


for r + 1 ≤ j ≤ s
(1 − p(t)) (1+β(t))t+c̃+cβ(t)
=
Ps
s

 (1 − p(t)) 1 − i=0 (i+β(t))ϕi (t)
for j = s + 1.
(1+β(t))t+c̃+cβ(t)
Ps
Finally, given Ld (pd (ϕ(t))) ≥ 0 is increasing in d, the identification of J ∞ in the display of
the proposition follows from monotone convergence.
We now give the proof of Theorem 1.1.2.
Proof of Theorem 1.1.2. Let Γ∞ ⊂
Q
i≥0 C([0, 1]; R),
endowed with the product topology,
be those elements ξ = hξ0 , ξ1 , . . .i such that
˙ i ≤ 1 for i ≥ 0, and
ξi (0) = ci , ξi (t) ≥ 0 is Lipschitz with constant 1, 0 ≤ [ξ(t)]
P
1 and i≥0 iξ˙i (t) ≤ 1 for almost all t.
P
˙
i≥0 ξi (t)
=
The spaces Γ∞ and Γ∗ are homeomorphic: Define F : Γ∞ → Γ∗ by F (ξ) = hξ 0 , . . . , ξ d , . . .i
P
where ξ d = hξ0 , . . . , ξd , ∞
l=d+1 ξl i. It is not difficult to see that F is a bi-continuous bijection.
Now, X n,∞ ∈ Γ∞ for n ≥ 1. Hence, through the action of F , the LDP for X n,∞ translates
to the LDP for Xn,∞ . We now identify the rate function. Given Propositions 1.3.1 and 1.3.2,
for a degree distribution ξ ∈ Γ∞ , we identify its rate as I ∞ (ξ) = J ∞ (F (ξ)). Since distributions
ξ 6∈ Γ∞ can never be attained, we set I ∞ (ξ) = ∞ in this case.
40
1.4
Proof of Theorem 1.1.3
Let d < ∞ be fixed. We first give some properties of ζ d (·) = hζ0 (·), ζ1 (·), . . . , ζ̄d+1 (·)i (cf.
(1.11)).
Lemma 1.4.1 (The zero-cost trajectory). We have ζ d ∈ Γd . In particular,
d
X
ζi (t) + ζ̄d+1 (t) = t + c,
i=0
and Id
(ζ d )
d
X
iζi (t) + (d + 1)ζ̄d+1 ≤ t + c̃,
(1.42)
i=0
= 0. Moreover
ζd
is the unique path with zero cost.
Proof. From the definition of ζ d and simple computations, we get (1.42).
For the uniqueness, suppose ζ (1) , ζ (2) are solutions to the system of ODEs (1.10) with initial
(1)
condition ζ d (0) = cd also satisfying (1.42). We show ζi
(1)
For i = 0, suppose ζ0
(1)
(2)
(2)
= ζi
using induction on 0 ≤ i ≤ d.
(1)
and ζ0 , we may assume that
6= ζ0 . Then, by continuity of ζ0
(2)
(2)
ζ0 (t) > ζ0 (t) for t ∈ [t0 , t1 ] for some 0 ≤ t0 < t1 ≤ 1. By the mean value theorem, there is a
(1)
(2)
t0 ∈ (t0 , t1 ) such that ζ̇0 (t0 ) − ζ̇0 (t0 ) > 0. But the ODE gives
(1)
(2)
ζ̇0 (t0 ) − ζ̇0 (t0 ) = −(1 − p(t0 ))
β(t0 )
(1)
(2)
(ζ (t0 ) − ζ0 (t0 )) < 0,
(1 + β(t0 ))t0 + c̃ + cβ(t0 ) 0
(1)
a contradiction. Now let i ≥ 1. Assume ζk
(1)
(2)
(1)
ζi (t) > ζi (t) for some t ∈ [0, 1]. As ζi
(1)
(2)
= ζk
(2)
and ζi
(1)
for k < i. Suppose ζi
(2)
6= ζi , say
are continuous, there exist 0 ≤ t0 < t1 ≤ 1
(2)
such that ζi (t) > ζi (t) for t ∈ [t0 , t1 ]. From the mean value theorem, there exists a t0 ∈ (t0 , t1 )
(1)
(2)
(1)
(2)
such that ζ̇i (t0 ) − ζ̇i (t0 ) > 0. On the other hand, by the induction hypothesis (ζi−1 = ζi−1 ),
from the ODE (1.10), we have
(1)
(2)
ζ̇i (t0 ) − ζ̇i (t0 ) = −(1 − p(t0 ))
(1)
i + β(t0 )
(1)
(2)
(ζ (t0 ) − ζi (t0 )) < 0,
(1 + β(t0 ))t0 + c̃ + cβ(t0 ) i
(2)
a contradiction. Now ζd+1 = ζd+1 from (1.42).
From the form of Id , any zero cost trajectory must satisfy the ODEs (1.10).
Proof of Theorem 1.1.3. Since the rate function Id has a unique minimizer, there exists a
δ > 0 such that I(ϕ) > ε > 0 for any ϕ ∈ Bδc (ζ d ) = {ϕ ∈ C([0, 1]; Rd+2 ) | ||ϕ − ζ d ||∞ ≥ δ}. The
LDP upper bound in Theorem 1.1.1 gives
lim sup
n→∞
1
log P {kXn − ζk∞ ≥ δ} ≤ − infc I(ϕ),
n
ϕ∈Bδ (ζ)
41
and the desired result follows from Borel-Cantelli Lemma.
1.5
Proof of Theorem 1.1.4
The proof of the theorem follows from the next lemma. Define, for positive real numbers
o1 , o2 , o3 , o4 , and o5 , the system of ODEs, O(o1 , o2 , o3 , o4 , o5 ): With initial condition ϕ(0) = cd
o3
ϕ0 (t)
·
1 + o4 t + o5
i + o3 ϕi (t)
= 1 − (1 − o2 )
·
for 1 ≤ i ≤ d.
1 + o4 t + o5
ϕ̇0 (t) = 1 − o1 − (1 − o2 )
[ϕ̇(t)]i
One can readily check that, when 0 < o2 < 1, χ(t) is the solution to O(o1 , o2 , o3 , o4 , o5 )
above where
χi (t) = bi (t + o5 ) +
i
X
ai,`
`=0
o (1−o2 ) `+o3
1+o4
5
t + o5
for 0 ≤ i ≤ d + 1.
Here, for given (o1 , o2 , o3 , o4 , o5 ), the sequences bi = bi (o1 , o2 , o3 , o4 , o5 ) and ai,` = ai,` (o1 , o2 , o3 , o4 , o5 )
o
are defined as b0 =
bi = b1
1−o1
o3
1+(1−o2 ) 1+o
, b1 =
4
i
3
Y
(1 − o2 ) `−1+o
1+o4
`=2
1 + (1 −
`+o3
o2 ) 1+o
4
3 b
o1 +(1−o2 ) 1+o
0
4
1+o
1+(1−o2 ) 1+o3
4
= b1
Γ(2 + o3 +
, and for i ≥ 0
1+o4
1−o2 )
Γ(1 + o3 )
Γ(i + o3 )
1
∼
1+o4 ,
1+o4
1+
Γ(i + 1 + o3 + 1−o2 )
i 1−o2
i − 1 + o3
ai−1,`
where 0 ≤ ` < i,
i−`
P
= ci − bi o5 − i−1
`=0 ai,` .
ai,` =
and ai,i
Recall the assumption for Theorem 1.1.4:
0 ≤ pmin ≤ p(·) ≤ pmax < 1, and 0 < βmin ≤ β(·) ≤ βmax < ∞.
e ζb are unique solutions of systems of ODEs,
Lemma 1.5.1 (Comparison Lemma). We have ζ,
O(pmin , pmax , βmin , βmax , max{c̃, c}), O(pmax , pmin , βmax , βmin , min{c̃, c}), respectively. Moreover,
for 0 ≤ i ≤ d and t ∈ [0, 1], with respect to the zero-cost trajectory ζ d (t) in Theorem 1.1.3 with
initial condition ζ d (0) = cd , we have
b i ≤ [ζ d (t)]i ≤ [ζ(t)]
e i.
[ζ(t)]
(1.43)
42
Proof. The proof that ζe and ζb are the unique solutions uses a similar argument to that used
in the proof of Lemma 1.4.1.
We now establish the inequality in the display with respect to ζe as an analogous proof works
b We use induction to see that [ζ]
e i ≥ [ζ]i for 0 ≤ i ≤ d.
for ζ.
e = ζ(0) = cd , from the ODEs, O(pmin , pmax , βmin , βmax , max{c̃, c}) and (1.10), we
Since ζ(0)
have
˙
ζe0 (t) − ζ̇0 (t) = p(t) − pmin
+ (1 − p(t))
β(t)ζ0 (t)
βmin ζe0 (t)
− (1 − pmax )
,(1.44)
(1 + β(t))t + c̃ + cβ(t)
(1 + βmax )(t + max{c̃, c})
˙
e
[ζ(t)]
i − [ζ̇(t)]i
= (1 − p(t))
(i + βmin )ζei (t)
(i + β(t))ζi (t)
− (1 − pmax )
. (1.45)
(1 + β(t))t + c̃ + cβ(t)
(1 + βmax )(t + max{c̃, c})
For i = 0, suppose ζe0 (t) < ζ0 (t) for some t. Then, by continuity, we may assume that ζe0 (t) <
ζ0 (t) for all t ∈ [t0 , t1 ] for some 0 ≤ t0 < t1 ≤ 1. From the mean value theorem, we find a t0 ∈
˙
˙
(t0 , t1 ) such that ζe0 (t0 ) < ζ̇0 (t0 ), which contradicts the ODE (1.44) as it gives ζe0 (t0 ) − ζ̇0 (t0 ) > 0.
Therefore, ζe0 ≥ ζ0 .
e i < [ζ(t)]i for some t. By induction hypothesis ([ζ(·)]
e i−1 ≥
Now, for 1 ≤ i ≤ d, suppose [ζ(t)]
e i , [ζ(·)]i , ζei (·) and ζi (·) are continuous functions,
[ζ(·)]i−1 ), we must have ζei (t) < ζi (t). Since [ζ(·)]
e i < [ζ(t)]i and ζei (t) < ζi (t) for all t ∈ [t0 , t1 ]
as for the case i = 0, we may assume that [ζ(t)]
e i − [ζ(t)]i , there is t0 ∈ (t0 , t1 )
for some 0 ≤ t0 < t1 ≤ 1. By the mean value theorem for [ζ(t)]
˙ 0
˙ 0
e
e
such that [ζ(t
)]i < [ζ̇(t0 )]i . But (1.45) gives [ζ(t
)]i − [ζ̇(t0 )]i > 0, a contradiction. Therefore
e i ≥ [ζ]i .
[ζ]
Proof of Theorem 1.1.4. Given Lemma 1.5.1, we need only detail the solutions ζe and ζb
when the initial configuration is ‘small’ and ‘large’ respectively. To this end, when the initial
e ζb are linear, namely
configuration is ‘small’ (ci ≡ 0), ζ,
ζei (t) = ebi t,
and ζbi (t) = bbi t,
where ebi := bi (pmin , pmax , βmin , βmax , max{c̃, c}) and bbi := bi (pmax , pmin , βmax , βmin , min{c̃, c}).
43
On the other hand, when the initial configuration is ‘large’ (ci > 0 for some 0 ≤ i ≤ d + 1),
e ζb are almost linear, namely as t ↑ ∞,
ζ,
ζei (t) = (ebi + o(1))t,
and ζbi (t) = (bbi + o(1))t.
44
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47
PART II
Graph coloring
48
INTRODUCTION
An edge-colored graph is called monochromatic if all its edges have the same color. An edgecolored graph is called rainbow or totally multicolored if all its edges have distinct colors. For a
coloring of the edges of a graph, classical Ramsey type problems involve determining whether a
given monochromatic subgraph exists. On the other hand, classical anti-Ramsey problems see
if a given rainbow subgraph exists. More precisely, the classical multicolor Ramsey function
Rk (G) is defined to be the smallest n such that any edge-coloring of a complete graph on n
vertices, Kn , in k colors contains a monochromatic copy of G, and the classical anti-Ramsey
function AR(n; H) is defined to be the largest number of colors in an edge-coloring of Kn not
containing a rainbow copy of H. The Ramsey and anti-Ramsey functions were introduced by
Ramsey [7] and by Erdős, Simonovits and Sós [2], respectively. These functions have found
applications in many other branches of mathematics and computer science (cf. Rosta [8]).
Since exact results are very difficult to find, a number of generalizations have sprung up over
the years (see, for example, Graham, Rothschild and Spencer [4], Fujita, Magnant and Ozeki
[3]). One of the generalizations is to investigate Ramsey properties involving a monochromatic
subgraph and a rainbow subgraph at the same time.
Let G and H be two graphs on fixed number of vertices. An edge coloring of a complete
graph, Kn , is called (G, H)-good if there is no monochromatic copy of G and no rainbow copy of
H in this coloring. As shown by Jamison and West [5], a (G, H)-good coloring of an arbitrarily
large complete graph exists unless either G is a star or H is a forest. Let S(n; G, H) be the set
of the number of colors, k, such that there is a (G, H)-good coloring of Kn with k colors. We
call S(n; G, H) a spectrum. Let max S(n; G, H), min S(n; G, H) be the maximum, minimum
number in S(n; G, H), respectively.
In Chapter 1, we study the following question:
49
Question 1 Given n, G and H, is S(n; G, H) interval? I.e. for any k with min S(n; G, H) ≤
k ≤ max S(n; G, H), is there a (G, H)-good coloring using k colors?
We show that the answer is YES for any n if G is not a star and H does not have a degree
1 vertex, or leaf. However we find that for some graphs G, H and some values of n, S(n; G, H)
can have a gap.
The function max S(n; G, H) is closely related to the classical anti-Ramsey function AR(n; H).
For graphs H which can not be vertex-partitioned into at most two induced forests, max S(n; G, H)
has been determined asymptotically by Axenovich and Iverson [1]. Determining max S(n; G, H)
is challenging for other graphs H, in particular for bipartite graphs or even for cycles.
Question 2 What is the value of max S(n; G, H) when H is bipartite?
The case when H is a cycle is studied in Chapter 2, where we show that for a large n, G
1
and a cycle of length k, Ck , max S(n; G, Ck ) = n k−2
+
2
k−1 + O(1) when G is either bipartite with ‘large’ parts, or a graph with chromatic number at least 3, and max S(n; G, Ck ) =
1
n s−2
2 + s−1 + g when G is bipartite with a ‘small’ part of size s, where g is a constant depending on G and k, not depending on n.
Similar to max S(n; ·, H), determining the classical anti-Ramsey function AR(n; H) is hard
when H is bipartite.
Question 3 What is the value of AR(n; H) when H is bipartite?
When H is a cycle of length k, Ck , Erdős, Simonovits and Sós [2] provided a rainbow Ck -free
1
coloring of edges of Kn with n k−2
2 + k−1 + O(1) colors and conjectured that this would be
optimal. Since then, the conjecture had been verified for small values of k by a series of papers
by Alon (1983), Schiermeyer (2001), Jiang and West (2003). Finally, Montellano-Ballesteros
and Neumann-Lara [6] proved the conjecture completely. In Chapter 3, we give a short proof.
We use the main technique used in [6] that proves each component of a graph representing the
coloring is Hamiltonian if each vertex has enough ‘new’ colors.
50
Bibliography
[1] M. Axenovich, P. Iverson, Edge-colorings avoiding rainbow and monochromatic subgraphs,
Discrete Math., 2008, 308(20), 4710–4723.
[2] P. Erdős, M. Simonovits, V. T. Sós, Anti-Ramsey theorems, Infinite and finite sets (Colloq.,
Keszthely, 1973; dedicated to P. Erdős on his 60th birthday), Vol. II, pp. 633–643. Colloq.
Math. Soc. Janos Bolyai, Vol. 10, North-Holland, Amsterdam, 1975.
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Graphs Combin. 26 (2010), no. 1, 1–30.
[4] R. L. Graham, B. L. Rothschild and J. H. Spencer, Ramsey Theory, John Wiley & Sons
(1980).
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Paper 89, 48 pp. (electronic).
51
CHAPTER 1.
A note on monotonicity of mixed Ramsey numbers
A paper submitted to Discrete Mathematics
Maria Axenovich and Jihyeok Choi
Abstract
For two graphs, G, and H, an edge-coloring of a complete graph is (G, H)-good if there
is no monochromatic subgraph isomorphic to G and no rainbow subgraph isomorphic to H
in this coloring. The set of number of colors used by some (G, H)-colorings of Kn is called a
mixed-Ramsey spectrum. This note addresses a fundamental question of whether the spectrum
is an interval. It is shown that the answer is “yes” if G is not a star and H does not contain a
pendent edge.
1.1
Introduction
Let G and H be two graphs on fixed number of vertices. An edge coloring of a complete
graph, Kn , is called (G, H)-good if there is no monochromatic copy of G and no rainbow (totally
multicolored) copy of H in this coloring. This, sometimes called mixed-Ramsey coloring, is a
hybrid of classical Ramsey and anti-Ramsey colorings, [18, 9]. As shown by Jamison and West
[15], a (G, H)-good coloring of an arbitrarily large complete graph exists unless either G is a
star or H is a forest.
Let S(n; G, H) be the set of the number of colors, k, such that there is a (G, H)-good coloring
of Kn with k colors. We call S(n; G, H) a spectrum. Let max S(n; G, H), min S(n; G, H) be the
maximum, minimum number in S(n; G, H), respectively. The behavior of these functions was
studied in [2], [8], [1] and others. Note that if there is no restriction on a graph H, S(n; G, ∗) is
52
an interval [k,
n
2
], where k is the largest number such that rk−1 (G) ≤ n, a classical multicolor
Ramsey number.
The main question investigated in this note is whether the same behavior continues to hold
for mixed Ramsey colorings. Specifically, for given integer n and graphs G and H, is S(n; G, H)
an interval? When G is not a star, for most graphs H, we show that S(n; G, H) is an interval.
Theorem 1.1.1. Let G be a graph that is not a star, and let H be a graph with minimum
degree at least 2. Then for any natural number n, S(n; G, H) is an interval.
The simplest connected graph H which is not a tree and which has a vertex of degree 1 is
K3 + e, a 4-vertex graph obtained by attaching a pendent edge to a triangle. We show that
S(n; G, K3 + e) could have a gap for some graphs G and some values of n. However, when n
is arbitrarily large, we do not have a single example of a graph G and a graph H for which
S(n; G, H) is not an interval.
Specifically, the next theorem is a collection of results on S(n; G, K3 + e). Here, `K2 is a
matching of size `, C4 is a 4-cycle, and P4 is a path on 4 vertices.
Theorem 1.1.2.
• S(n; `K2 , K3 ) = S(n; `K2 , K3 + e) = [d n−2`+1
`−1 e + 1, n − 1], n ≥ 4,
S(n; P4 , K3 ) = S(n; P4 , K3 + e) = [n − 2, n − 1], n ≥ 4,
S(n; C4 , K3 ) = S(n; C4 , K3 + e) = [n − 3, n − 1], n ≥ r3 (C4 ) = 11,
S(n; K3 , K3 ) = S(n; K3 , K3 + e) = [c log n, n − 1], n ≥ r3 (K3 ) = 17,
S(n; K1,` , K3 ) = S(n; K1,` , K3 + e) = ∅, n ≥ 3`.
• S(10; C4 , K3 + e) = {3, 7, 8, 9}.
Corollary 1.1.3. If ` ≥ 2 and n ≥ max{17, 3` + 1}, then S(n; G, K3 + e) is an interval for
any G ∈ {K3 , `K2 , C4 , P4 , K1,` }. However, S(n; G, K3 + e) is not an interval if n = 10 and
G = C4 .
53
Open question. Are there graphs G and H such that for any natural number N there is
n > N so that S(n; G, H) is not an interval?
1.2
Definitions and proofs of main results
For an edge coloring c of Kn and a vertex x ∈ V (Kn ), let Nc (x) be the set of colors used only
on edges incident to x, and for X ⊆ V (Kn ) let c(X) be the set of colors used on edges induced
by X. Let |c| denote the number of colors used in the coloring c. Then |c| = |Nc (x)| + |c(V \ x)|
for any x ∈ V .
Observation 1 If G is not a star, and A and B are color classes which are stars with the
same center in a (G, H)-good coloring c of Kn with k colors, then replacing A and B in c with
a new color class A ∪ B gives a (G, H)-good coloring using k − 1 colors.
Observation 2 For any graphs G and H,
min S(n; G, H) ≤ min S(n + 1, G, H).
Proof. Consider a (G, H)-good coloring of Kn+1 with k colors. Delete one vertex to get a
(G, H)-good coloring of Kn with k 0 ≤ k colors.
Observation 3 For G ⊆ G0 and H ⊆ H 0 ,
S(n; G, H) ⊆ S(n; G0 , H) ⊆ S(n; G0 , H 0 )
and
S(n; G, H) ⊆ S(n; G, H 0 ) ⊆ S(n; G0 , H 0 ).
Proof. If there is no monochromatic G and no rainbow H in a coloring of E(Kn ), then there
is no monochromatic G0 and no rainbow H 0 in this coloring.
Observation 4 If G is not a star, H has minimum degree at least 2, and k ∈ S(n; G, H), then
k + 1 ∈ S(n + 1; G, H).
54
Proof. Consider a (G, H)-good coloring of Kn with k colors. Add a new vertex x, and color
edges incident to x by a new color to get a (G, H)-good coloring of Kn+1 with k + 1 colors.
Proof of Theorem 1.1.1.
We need to prove that [min S(n; G, H), max S(n; G, H)] ⊆ S(n; G, H). We use induction
on n. When n = 2, any coloring uses one color. Let n ≥ 3. Consider the smallest k such
that [k, max S(n; G, H)] ⊆ S(n; G, H). Observe that in any (G, H)-good k-coloring of Kn and
any vertex x, we have |Nc (x)| ≤ 1, otherwise applying Observation 1 gives us a (G, H)-good
(k − 1)-coloring of Kn violating minimality of k. Consider a (G, H)-good k-coloring of Kn and
any vertex x, and delete it. Then we have a (G, H)-good coloring of Kn−1 with k or k − 1
colors. Here we note that max S(n − 1; G, H) ≥ k − 1. By induction, S(n − 1; G, H) is an
interval, i.e., [min S(n − 1; G, H), max S(n − 1; G, H)] = S(n − 1; G, H). Then by Observation
4, [min S(n − 1; G, H) + 1, max S(n − 1; G, H) + 1] ⊆ S(n; G, H). Since min S(n; G, H) ≥
min S(n − 1; G, H) from Observation 2, [min S(n; G, H), max S(n − 1; G, H) + 1] ⊆ S(n; G, H).
Since k ≤ max S(n − 1; G, H) + 1 and [k, max S(n; G, H)] ⊆ S(n; G, H) we finally have that
[min S(n; G, H), max S(n; G, H)] ⊆ S(n; G, H).
For the proof of Theorem 1.1.2, we shall use the function
f (k; G, H) = max{n : there is a (G, H)-good coloring of Kn using exactly k colors}.
Note that if f (k; G, H) = n, then min S(n; G, H) ≤ k.
Observation 5 If f (k; G, H) = n and f (k̃; G, H) < n for any k̃ < k, then min S(n; G, H) = k.
In particular, if f is strictly increasing in k, then f (k; G, H) = n implies min S(n; G, H) = k.
Proof of Theorem 1.1.2.
First observe that max S(n; G, H) ≤ AR(n, H), where AR(n, H) is the classical anti-Ramsey
55
number, the maximum number of colors in an edge-coloring of Kn with no rainbow subgraphs
isomorphic to H. If G is not a star, max S(n; G, K3 ) = AR(n, K3 ) = n − 1, see [2]. Moreover,
from Observation 3, we obtain that max S(n; G, K3 ) ≤ max S(n; G, K3 + e); and from [12],
we know that AR(n, K3 ) = AR(n, K3 + e). Thus, when G is not a star, max S(n; G, K3 ) =
max S(n; G, K3 + e) = n − 1 for n ≥ 4.
Therefore if min S(n; G, K3 ) = min S(n, G, K3 + e), and G is not a star, we can conclude
that S(n; G, K3 + e) = S(n; G, K3 ), which is an interval by Theorem 1. Next, we shall analyze
min S(n, G, K3 + e). We note that f (k; G, H) + 1 ≤ rk (G), where rk (G) denotes the classical
k-color Ramsey number for G. The equality holds if there is a k-coloring of E(Krk (G)−1 ) with
no monochromatic G and no rainbow H.
Case 1. G = `K2
From [17], we have that rk (`K2 ) = (k − 1)(` − 1) + 2`. The extremal coloring providing
this Ramsey number can be constructed as follows. Consider a complete graph on 2` − 1
vertices colored entirely with color 1, add ` − 1 vertices and color all edges incident to these
vertices with color 2, then add another ` − 1 vertices and color all edges incident to these
vertices with color 3. Repeat this process until we get a k-coloring of a complete graph on
2` − 1 + (k − 1)(` − 1) vertices which contains no monochromatic `K2 . Note that this coloring
contains no rainbow cycles, thus, it contains neither rainbow copy of K3 nor rainbow copy of
K3 + e. Hence f (k; `K2 , H) = f (k; `K2 , H + e) = (k − 1)(` − 1) + 2` − 1 for any H, not a
forest. By Observation 5, min S(n; `K2 , H) = min S(n; `K2 , H + e). In particular for ` ≥ 2,
min S(n; `K2 , K3 ) = min S(n; `K2 , K3 + e) = d n−2`+1
`−1 e + 1.
Case 2. G ∈ {K3 , P4 , C4 }
From [5, 2, 13, 7, 8] we have that f (k; K3 , K3 ) = f (k; K3 , K3 + e) = λ(k), for k ≥ 1 and k 6= 3,
f (3; K3 , K3 ) = 10, and f (3; K3 , K3 + e) = r3 (K3 ) − 1 = 16, where λ(k) = 5k/2 if k is even,
2 · 5(k−1)/2 if k is odd; f (k; P4 , K3 ) = f (k; P4 , K3 + e) = k + 2 for k ≥ 1; and f (k; C4 , K3 ) =
f (k; C4 , K3 +e) = k+3, for k = 2 or k ≥ 4, f (3; C4 , K3 ) = 6, and f (k; C4 , K3 +e) = r3 (C4 )−1 =
10. Therefore from Observation 5, min S(n; P4 , K3 ) = min S(n; P4 , K3 + e) = n − 2 for n ≥ 4,
min S(n; C4 , K3 ) = min S(n; C4 , K3 + e) = n − 3 for n ≥ r3 (C4 ) = 11, and min S(n; K3 , K3 ) =
56
min S(n; K3 , K3 +e) = c log n for n ≥ r3 (K3 ) = 17. Thus min S(n; G, K3 ) = min S(n; G, K3 +e)
for G ∈ {K3 , P4 , C4 } and n ≥ r3 (G).
Case 3. G = K1,`
In [14], it was shown that any coloring of E(Kn ) with no rainbow triangles has a monochromatic star K1,2n/5 . Using this fact and the pigeonhole principle, we easily see that any coloring
of E(Kn ) with no rainbow K3 + e has a monochromatic star K1,n/3 . Namely, let c be a coloring
of E(Kn ) with no rainbow K3 + e. Since 2n/5 ≥ n/3, we may assume there is a rainbow copy T
of K3 . To avoid a rainbow K3 + e in this coloring, the edges between V (T ) and V (Kn ) − V (T )
have colors only presented on the edges of T , i.e., at most three colors. Then by pigeonhole
principle, we can find a monochromatic star K1,s with one vertex in V (T ) and s ≥
n−3
3
vertices
in V (Kn ) − V (T ). Considering other two vertices in V (T ), we finally have a monochromatic
star K1,n/3 . This is sharp as is seen in [8], namely there is a (K1,s , K3 + e)-good coloring of
E(K3s−2 ). Therefore S(n; K1,` , K3 ) = S(n; K1,` , K3 + e) = ∅ if n ≥ 3`.
Summarizing 1), 2), and 3) we have that S(n; G, K3 ) = S(n; G, K3 + e) is an interval if G is
one of {`K2 , K3 , P4 , C4 , K1,` } and n ≥ N , where N is a constant depending only on G. This
concludes the proof of the first part of the Theorem.
Consider the case when G = C4 , H = K3 +e and n = 10. Since r2 (C4 ) = 6 < 10, we see that
there is no (C4 , K3 +e)-good coloring of K10 in two colors. On the other hand, since r3 (C4 ) = 11,
there is a (C4 , K3 + e)-good coloring of K10 in three colors. Thus min S(10; C4 , K3 + e) = 3.
We also have that max S(10; C4 , K3 + e) = AR(10, K3 ) = 9. Since f (k; C4 , K3 + e) = k + 3 < 10
for 4 ≤ k ≤ 6, there is no (C4 , K3 + e)-good coloring of K10 with 4, 5, or 6 colors. To construct
8- and 7-colorings of K10 with no rainbow K3 + e and no monochromatic C4 , consider a vertex
set {v1 , . . . , v10 }. Let c(vi vj ) = i, 1 ≤ i ≤ 7, i < j; c(v8 v9 ) = c(v8 v10 ) = c(v9 v10 ) = 8. Let
c0 (vi vj ) = i, 1 ≤ i ≤ 5, i < j; c0 (v6 v7 ) = c0 (v7 v8 ) = c0 (v8 v9 ) = c0 (v9 v10 ) = c0 (v10 v6 ) = 6,
all other edges get color 7 under c0 . Note that c and c0 are 8- and 7-colorings, respectively,
containing no rainbow K3 and no monochromatic C4 . Thus S(10; C4 , K3 + e) = {3, 7, 8, 9}.
57
Acknowledgments:
The authors thank the anonymous referee for careful reading and
comments improving the manuscript.
58
Bibliography
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Subgraph, Electron. J. Combin. 17 (1) (2010), Research Paper 31, 15pp.
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(2005), 145–160.
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[5] F. R. K. Chung, R. L. Graham, Edge-colored complete graphs with precisely colored subgraphs, Combinatorica 3 (1983), no. 3-4, 315–324.
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no. 3, 333–339.
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264–286
60
CHAPTER 2.
On colorings avoiding a rainbow cycle and a fixed
monochromatic subgraph
A paper published in the Electronic Journal of Combinatorics
Maria Axenovich and Jihyeok Choi
Abstract
Let H and G be two graphs on fixed number of vertices. An edge coloring of a complete
graph is called (H, G)-good if there is no monochromatic copy of G and no rainbow (totally multicolored) copy of H in this coloring. As shown by Jamison and West, an (H, G)-good coloring
of an arbitrarily large complete graph exists unless either G is a star or H is a forest. The largest
number of colors in an (H, G)-good coloring of Kn is denoted maxR(n; G, H). For graphs H
which can not be vertex-partitioned into at most two induced forests, maxR(n; G, H) has been
determined asymptotically. Determining maxR(n; G, H) is challenging for other graphs H, in
particular for bipartite graphs or even for cycles. This manuscript treats the case when H is a
cycle. The value of maxR(n; G, Ck ) is determined for all graphs G whose edges do not induce
a star.
2.1
Introduction and main results
For two graphs G and H, an edge coloring of a complete graph is called (H, G)-good if there
is no monochromatic copy of G and no rainbow (totally multicolored) copy of H in this coloring.
The mixed anti-Ramsey numbers, maxR(n; G, H), minR(n; G, H) are the maximum, minimum
number of colors in an (H, G)-good coloring of Kn , respectively. The number maxR(n; G, H)
is closely related to the classical anti-Ramsey number AR(n, H), the largest number of colors in
61
an edge-coloring of Kn with no rainbow copy of H introduced by Erdős, Simonovits and Sós [9].
The number minR(n; G, H) is closely related to the classical multicolor Ramsey number Rk (G),
the largest n such that there is a coloring of edges of Kn with k colors and no monochromatic
copy of G. The mixed Ramsey number minR(n; G, H) has been investigated in [3, 13, 11].
This manuscript addresses maxR(n; G, H). As shown by Jamison and West [14], an (H, G)good coloring of an arbitrarily large complete graph exists unless either G is a star or H is a
forest. Let a(H) be the smallest number of induced forests vertex-partitioning the graph H.
This parameter is called a vertex arboricity. Axenovich and Iverson [3] proved the following.
Theorem 2.1.1. Let G be a graph whose edges do not induce a star and H be a graph with
2
1
a(H) ≥ 3. Then maxR(n; G, H) = n2 1 − a(H)−1
(1 + o(1)).
When a(H) = 2, the problem is challenging and only few isolated results are known [3].
Even in the case when H is a cycle, the problem is nontrivial. This manuscript addresses this
case. Since (Ck , G)-good colorings do not contain rainbow Ck , it follows that
k−2
1
maxR(n; G, Ck ) ≤ AR(n, Ck ) = n
+
+ O(1),
2
k−1
(2.1)
where the equality is proven by Montellano-Ballesteros and Neumann-Lara [16]. We show
that maxR(n; G, Ck ) = AR(n; Ck ) when G is either bipartite with large enough parts, or a
graph with chromatic number at least 3. In case when G is bipartite with a “small” part,
maxR(n; G, Ck ) depends mostly on G, namely, on the size of the “small” part. Below is the
exact formulation of the main result.
If a graph G is bipartite, we let s(G) = min{s : G ⊆ Ks,r , s ≤ r for some r} and t(G) =
|V (G)| − s(G). I.e., s(G) is the sum of the sizes of smaller parts over all components of G.
Theorem 2.1.2. Let k ≥ 3 be an integer and G be a graph whose edges do not induce a star.
Let s = s(G) and t = t(G) if G is bipartite. There are constants n0 = n0 (G, k) and g = g(G, k)
such that for all n ≥ n0
maxR(n; G, Ck ) =



n
k−2
2
+
1
k−1


n
s−2
2
+
1
s−1
+ O(1),
+ g,
if χ(G) = 2 and s ≥ k or χ(G) ≥ 3
otherwise
62
Here g = g(G, k) = ER2 s + t, 3sk + t + 1, k , where the number ER denotes the ErdősRado number stated in section 2. Note that it is sufficient to take g(G, k) = 2c`
2
log ` ,
where
` = 3sk + t + 1.
We give the definitions and some observations in section 2.2, the proof of the main theorem
in section 2.3 and some more accurate bounds for the case when H = C4 in the last section of
the manuscript.
2.2
Definitions and preliminary results
First we shall define a few special edge colorings of a complete graph: lexical, weakly lexical,
k-anticyclic, c∗ and c∗∗ .
Let c : E(Kn ) → N be an edge coloring of a complete graph on n vertices for some fixed n.
We say that c is a weakly lexical coloring if the vertices can be ordered v1 , . . . , vn , and the
colors can be renamed such that there is a function λ : V (Kn ) → N, and c(vi vj ) = λ(vmin{i,j} ),
for 1 ≤ i, j ≤ n. In particular, if λ is one to one, then c is called a lexical coloring.
We say that c is a k-anticyclic coloring if there is no rainbow copy of Ck , and there is a
partition of V (Kn ) into sets V0 , V1 , . . . , Vm with 0 ≤ |V0 | < k − 1 and |V1 | = · · · = |Vm | = k − 1,
n
where m = b k−1
c, such that for i, j with 0 ≤ i < j ≤ m, all edges between Vi and Vj have
the same color, and the edges spanned by each Vi , i = 0, . . . , m have new distinct colors using
pairwise disjoint sets of colors.
We denote a fixed coloring from the set of k-anticyclic colorings of Kn such that the color
of any edges between Vi and Vj is min{i, j} by c∗ .
Finally, we need one more coloring, c∗∗ , of Kn . Let c∗∗ be a fixed coloring from the set of
the following colorings of E(Kn ); let the vertex set V (Kn ) be a disjoint union of V0 , V1 , . . . , Vm
with 0 ≤ |V0 | < s − 1, |V1 | = · · · = |Vm−1 | = s − 1, and |Vm | = k − 1, where m − 1 = b n−k+1
s−1 c.
Let the color of each edge between Vi and Vj for 0 ≤ i < j ≤ m be i. Color the edges spanned
by each Vi , i = 0, . . . , m with new distinct colors using pairwise disjoint sets of colors.
For a coloring c, let the number of colors used by c be denoted by |c|. Observe that
c∗ is a blow-up of a lexical coloring with parts inducing rainbow complete subgraphs. Any
63
monochromatic bipartite subgraph in c∗ and c∗∗ is a subgraph of Kk−1,t and Ks−1,t for some
t, respectively. Also we easily see that if c is k-anticyclic, then
1
k−2
+
+ O(1),
|c| ≤ |c | = n
2
k−1
s−2
1
∗∗
|c | = n
+ O(1).
+
2
s−1
∗
(2.2)
(2.3)
Let K = Kn . For disjoint sets X, Y ⊆ V , let K[X] be the subgraph of K induced by X,
and let K[X, Y ] be the bipartite subgraph of K induced by X and Y . Let c(X) and c(X, Y )
denote the sets of colors used in K[X] and K[X, Y ], respectively by a coloring c.
Next, we state a canonical Ramsey theorem which is essential for our proofs.
Theorem 2.2.1 (Deuber [7], Erdős-Rado [8]). For any integers m, l, r, there is a smallest
integer n = ER(m, l, r), such that any edge-coloring of Kn contains either a monochromatic
copy of Km , a lexically colored copy of Kl , or a rainbow copy of Kr .
The number ER is typically referred to as Erdős-Rado number, with best bound in the
2
symmetric case provided by Lefmann and Rödl [15], in the following form: 2c1 ` ≤ ER(`, `, `) ≤
2c2 `
2
log ` ,
for some constants c1 , c2 .
2.3
Proof of Theorem 2.1.2
If G is a graph with chromatic number at least 3, then maxR(n; G, Ck ) = n
k−2
2
+
1
k−1
+
O(1) as was proven in [3].
For the rest of the proof we shall assume that G is a bipartite graph, not a star, with
s = s(G), t = t(G), and G ⊆ Ks,t . Note that 2 ≤ s ≤ t. Let K = Kn . If s ≥ k, then the lower
bound on maxR(n; G, Ck ) is given by c∗ , a special k-anticyclic coloring. The upper bound
follows from (2.1).
Suppose s < k. The lower bound is provided by a coloring c∗∗ . Since maxR(n; G, Ck ) ≤
maxR(n; Ks,t , Ck ), in order to provide an upper bound on maxR(n; G, Ck ), we shall be giving
64
an upper bound on maxR(n; Ks,t , Ck ).
The idea of the proof is as follows. We consider an edge coloring c of K = (V, E) with no
monochromatic Ks,t and no rainbow Ck , and estimate the number of colors in this coloring
by analyzing specific vertex subsets: L, A, B, where L is the vertex set of the largest weakly
lexically colored complete subgraph, A is the set of vertices in V \ L which “disagrees” with
coloring of L on some edges incident to the initial part of L, and B is the set of vertices in
V \ L which “disagrees” with coloring of L on some edges incident to the terminal part of L.
Let V 0 = V \ L. We are counting the colors in the following order: first colors induced by V 0
which are not used on any edges incident to L or any edges induced by L, then colors used on
edges between V 0 and L which are not induced by L, finally colors induced by L.
Now, we provide a formal proof. Assume that n is sufficiently large such that n ≥ ER(s +
t, 3sk + t + 1, k). Let c be a coloring of E(K) with no monochromatic copy of Ks,t and no
rainbow copy of Ck , c : E(K) → N. Then there is a lexically colored copy of K3sk+t+1 by
the canonical Ramsey theorem. Let L be a vertex set of a largest weakly lexically colored
Kq , q ≥ 3sk + t + 1, say L = {x1 , . . . , xq } and c(xi xj ) = λ(xi ) for 1 ≤ i < j ≤ q, for some
function λ : L → N. If X = {xi1 , . . . , xi` } ⊆ L and λ(xi1 ) = · · · = λ(xi` ) = j for some j,
then we denote λ(X) = j. We write, for i ≤ j, xi Lxj := {xi , xi+1 , . . . , xj }, and for i > j,
xi Lxj := {xi , xi−1 , . . . , xj }. We say that xi precedes xj if i < j.
Let Tt , Tsk+t , T2sk+t , and T3sk+t be the tails of L of size t, sk + t, 2sk + t, and 3sk + t
respectively, i.e.,
Tt := {xq−t+1 , xq−t+2 , . . . , xq },
Tsk+t := {xq−sk−t+1 , xq−sk−t+2 , . . . , xq },
T2sk+t := {xq−2sk−t+1 , xq−2sk−t+2 , . . . , xq },
T3sk+t := {xq−3sk−t+1 , xq−3sk−t+2 , . . . , xq },
see Figure 2.1.
We shall use these tails to count the number of colors: the common difference, sk, of sizes
65
T 3sk + t
L
Figure 2.1
T 2sk + t
T sk + t
Tt
Tt , Tsk+t , T2sk+t , and T3sk+t
of tails is from observations below(Claims 0.1–0.3). The first tail Tt is used in Claims 0.1 – 0.3
and to find monochromatic copy of Ks,t . The third tail T2sk+t is the main tool used in Part
1, 2 of the proof, it helps finding rainbow copy of Ck . The other tails Tsk+t and T3sk+t are
for technical reasons used in Claim 2.1 and Claim 1.3, respectively. Note that the size of the
fourth tail is used in the second parameter of Erdős-Rado number bounding n.
We start by splitting the vertices in V \ L according to “agreement” or “disagreement” of
a corresponding colors used in L \ T2sk+t and in edges between L and V \ L. Formally, let
V 0 = V \ L, and
A := {v ∈ V 0 | there exists y ∈ L \ T2sk+t such that c(vy) 6= λ(y)},
B := {v ∈ V 0 | c(vx) = λ(x), x ∈ L \ T2sk+t ,
and there exists y ∈ T2sk+t \ {xq } such that c(vy) 6= λ(y)}.
Note that V 0 − A − B = {v ∈ V 0 | c(vx) = λ(x), x ∈ L \ {xq }} = ∅ since otherwise L is not
the largest weakly colored complete subgraph. Thus
V = L ∪ A ∪ B.
Let c0 := c(L) ∪ c(V
0 , L).
In the first part of the proof we bound c(B) ∪ c(B, A) \ c0 +
|c(B, L) \ c(L)|, in the second part we bound |c(A) \ c0 | + |c(A, L) \ c(L)| + |c(L)|.
Claim 0.1 Let x ∈ L \ Tt . Then |{y ∈ L \ Tt | λ(x) = λ(y)}| ≤ s − 1 < s.
If this claim does not hold, the corresponding y’s and Tt induce a monochromatic Ks,t .
Claim 0.2 Let y, y 0 ∈ L \ Tt such that |yLy 0 | > (s − 1)` + 1 for some ` ≥ 0. Then
|c(yLy 0 )| ≥ ` + 1.
66
It follows from Claim 0.1.
Claim 0.3 Let v, v 0 ∈ V 0 and y, y 0 ∈ L \ Tt such that y precedes y 0 . Let P be a rainbow
path from v to v 0 in V 0 with 1 ≤ |V (P )| ≤ k − 2 and colors not from c0 . If c(vy) 6= λ(y),
c(v 0 y 0 ) 6∈ {c(vy), λ(y)}, and |yLy 0 | > (s − 1)(k − |V (P )|) + 1, then there is a rainbow Ck induced
by V (P ) ∪ yLy 0 .
Indeed, by Claim 0.2, |c(yLy 0 )| ≥ k − |V (P )| + 1. Hence |c(yLy 0 ) \ {c(vy), c(v 0 y 0 )}| ≥
k − |V (P )| − 1. So we can find a rainbow path on k − |V (P )| vertices in L with endpoints y
and y 0 of colors from c(yLy 0 ) \ {c(vy), c(v 0 y 0 )}, which together with V (P ) induce a rainbow Ck
since colors of P are not from c0 .
PART 1
We shall show that c(B) ∪ c(B, A) \ c0 + |c(B, L) \ c(L)| ≤ const = const(k, s, t).
Claim 1.1 |B| < ER(s + t, 2sk + t + 1, k).
Suppose |B| ≥ ER(s + t, 2sk + t + 1, k). Then there is a lexically colored copy of a complete
subgraph on a vertex set Y ⊆ B of size 2sk + t + 1. Then (L ∪ Y ) \ T2sk+t is weakly lexical,
which contradicts the maximality of L.
Claim 1.2 |c(B, L) \ c(L)| ≤ (2sk + t)|B|.
|c(B, L) \ c(L)| ≤ |c(B, T2sk+t )| ≤ (2sk + t)|B| by the definition of B.
Claim 1.3 c(B) ∪ c(B, A) \ c0 <
ER(s+t,3sk+t+1,k)
2
.
Let A = A1 ∪ A2 , where A1 := {v ∈ A | there exists y ∈ L \ T3sk+t with c(vy) 6= λ(y)},
and A2 := A \ A1 .
67
L
T2sk + t
T3sk + t
y
A1
Figure 2.2
y’
v
v’
B
A rainbow Ck in Claim 1.3
First, we show that c(B, A1 ) ⊆ c0 . Assume that c(v 0 v) 6∈ c0 for some v ∈ A1 and v 0 ∈ B
with c(vy) 6= λ(y) for some y ∈ L \ T3sk+t and c(v 0 x) = λ(x) for any x ∈ L \ T2sk+t . From Claim
0.1, we can find y 0 , one of the last 2s − 1 elements in T3sk+t \ T2sk+t such that λ(y 0 ) is neither
c(vy) nor λ(y). Since λ(y 0 ) = c(v 0 y 0 ), we have that c(v 0 y 0 ) 6∈ {c(vy), λ(y)}. Moreover we have
|yLy 0 | > (s − 1)(k − 2) + 1. By Claim 0.3, there is a rainbow Ck induced by {v, v 0 } ∪ yLy 0 , see
Figure 2.2.
Second, we shall observe that |A2 ∪ B| < ER(s + t, 3sk + t + 1, k) by the argument similar
to one used in Claim 1.1. We see that otherwise A2 ∪ B contains a lexically colored complete
subgraph on 3sk + t + 1 vertices, which together with L − T3sk+t gives a larger than L weakly
lexically colored complete subgraph.
PART 2
We shall show that |c(A) \ c0 | + |c(A, L) \ c(L)| + |c(L)| ≤ n
s−2
2
+
1
s−1
.
In order to count the number of colors in A and (A, L), we consider a representing graph of
these colors as follows. First, consider a set E 0 of edges from K[A] having exactly one edge of
each color from c(A) \ c0 . Second, consider a set of edges E 00 from the bipartite graph K[A, L]
having exactly one edge of each color from c(A, L) \ c(L). Let G be a graph with edge-set
E 0 ∪ E 00 spanning A. Then |c(A) \ c0 | + |c(A, L) \ c(L)| = |E(G)|.
We need to estimate the number of edges in G. Let A1 , . . . , Ap be sets of vertices of the
68
L
G’
1
A1
G’
2
A2
G’
3
G’
4
G"
1
G’
5
G"
2
A3
G"
3
Ap
Figure 2.3
G1 and G2
connected components of G[A]. Let L1 , . . . , Lp be sets of the neighbors of A1 , . . . , Ap in L
respectively, i.e., for 1 ≤ i ≤ p, Li := {x ∈ L |{x, y} ∈ E(G) for some y ∈ Ai }. Let
G1 :=
[
G[Ai ],
i : |E(G[Ai ,Li ])|≤1
G2 :=
[
G[Ai ∪ Li ].
i : |E(G[Ai ,Li ])|≥2
Let G01 , . . . , G0p1 be the connected components of G1 , and let G001 , . . . , G00p2 be the connected
components of G2 . See Figure 2.3 for an example of G1 and G2 .
Claim 2.1 We may assume that V (G) ∩ L ⊆ L \ Tsk+t .
For a fixed v ∈ A, let ω be a color in c(v, L) \ c(L), if such exists. Let L(ω) := {x ∈
L | c(vx) = ω}. Suppose L(ω) ⊆ Tsk+t . Since v ∈ A, there exists y ∈ L \ T2sk+t such that
c(vy) 6= λ(y). Let y 0 ∈ L(ω) ⊆ Tsk+t . Then c(vy 0 ) 6∈ {c(vy), λ(y)}. Since |yLy 0 | > (s − 1)k + 1 >
(s − 1)(k − 1) + 1, there is a rainbow Ck induced by {v} ∪ yLy 0 by Claim 0.3, see figure 4.
Therefore L(ω) ∩ (L \ Tsk+t ) 6= ∅. Hence we can choose edges for the edge set E 00 of G only
from K[A, L \ Tsk+t ].
Claim 2.2 For every i, 1 ≤ i ≤ p, K[Ai , Tt ] is monochromatic; for every j, 1 ≤ j ≤ p2 ,
K[V (G00j ), Tt ] is monochromatic. In particular, for every h, 1 ≤ h ≤ p1 , K[V (G0i ), Tt ] is
monochromatic.
1. Fix i, 1 ≤ i ≤ p. We show that K[Ai , Tt ] is monochromatic. Let v ∈ Ai and y ∈ L\T2sk+t
with c(vy) 6= λ(y).
69
T2sk + t
L
Tsk + t
y
y’
v
Figure 2.4
A rainbow Ck in Claim 2.1 and Claim 2.2-1.(1)
T sk + t
L
z
Tt
x
v
Figure 2.5
A rainbow Ck in Claim 2.2-1.(2)
(1) For any y 0 ∈ Tsk+t , c(vy 0 ) is either c(vy) or λ(y). Indeed if c(vy 0 ) 6∈ {c(vy), λ(y)}, then
there is a rainbow Ck induced by {v} ∪ yLy 0 by Claim 0.3, see Figure 2.4.
(2) |c(v, Tt )| = 1. Indeed, let Ly = {x ∈ Tsk+t \ Tt | λ(x) 6= c(vy) and λ(x) 6= λ(y)}. Then by
Claim 0.1, |Ly | ≥ |Tsk+t \ Tt | − 2(s − 1) + 1 > (s − 1)(k − 3) + 1. Hence |c(Ly )| ≥ k − 2
by Claim 0.2. Let z be the vertex in Ly preceding every other vertex in Ly . Suppose
there is x ∈ Tt such that c(vx) 6= c(vz). Since c(Ly ) ⊆ c(zLx), there exists a rainbow
path from z to x on k − 1 vertices in Tsk+t of colors disjoint from {c(vy), λ(y)}. So
there is a rainbow Ck induced by {v} ∪ zLx, see Figure 2.5. Therefore for any x ∈ Tt ,
c(vx) = c(vz) ∈ {c(vy), λ(y)}.
(3) For any neighbor v 0 of v in G[Ai ], if such exists, c(v 0 , Tt ) = c(v, Tt ). Indeed, we see that
for any y 0 ∈ Tsk+t , c(v 0 y 0 ) ∈ {c(vy), λ(y)}, otherwise there is a rainbow Ck induced by
{v, v 0 }∪yLy 0 by Claim 0.3. Also we see that for any x ∈ Tt , c(v 0 x) = c(vz) ∈ {c(vy), λ(y)},
where z is defined above; otherwise there is a rainbow Ck induced by {v, v 0 } ∪ zLx, see
Figure 2.6. Therefore c(v 0 , Tt ) = c(v, Tt ).
70
L
z
y
y’
x
v’
v
Figure 2.6
Tt
T sk + t
Rainbow Ck ’s in Claim 2.2-1.(3)
T sk + t
L
x
x’
P
v
Figure 2.7
v’
Rainbow Ck ’s in Claim 2.2-2.(1): red when |P | = k − 2, green when |P | < k − 2.
(4) Since G[Ai ] is connected, K[Ai , Tt ] is monochromatic of color c(vz).
Note that to avoid a monochromatic Ks,t , we must have that |Ai | ≤ s − 1 ≤ k − 2 for
1 ≤ i ≤ p.
2. Fix j, 1 ≤ j ≤ p2 . We show that K[V (G00j ), Tt ] is monochromatic.
(1) K[V (G00j ) ∩ L, Tt ] is monochromatic. Indeed, since G00j , a connected component of G, is
a union of G[Ai ∪ Li ]’s satisfying |E(G[Ai , Li ])| ≥ 2, by the connectivity, it is enough to
show that λ(x) = λ(x0 ) for any x, x0 ∈ Li for Li in G00j , where x precedes x0 . From Claim
2.1, we may assume that x, x0 are in L \ Tsk+t . Suppose λ(x) 6= λ(x0 ). Let v, v 0 ∈ Ai such
that {v, x} and {v 0 , x0 } are edges of G (possibly v = v 0 ). Let P denote a set of vertices
on a path from v to v 0 in G[Ai ]. Then 1 ≤ |P | ≤ k − 2 since |Ai | ≤ k − 2. If |P | = k − 2,
then P ∪ {x, x0 } induces a rainbow Ck , otherwise so does P ∪ {x} ∪ x0 Lxq from Claim 0.3,
see Figure 2.7. Therefore λ(x) = λ(x0 ).
(2) K[V (G00j ), Tt ] is monochromatic. To prove this, consider i such that G[Ai , Li ] ⊆ G00j .
Observe first that K[Ai , Tt ] and K[Li , Tt ] are monochromatic by 1.(4) and 2.(1). Next,
71
x
L
x’
v
Figure 2.8
T sk + t
Tt
v’
Rainbow Ck ’s for Claim 2.2-2.(2)
we shall show that c(Ai , Tt ) = λ(Li ). Suppose c(Ai , Tt ) 6= λ(Li ) for some i such that
G[Ai ∪ Li ] ⊆ G00j . Let v, v 0 ∈ Ai and x, x0 ∈ Li such that {v, x} and {v 0 , x0 } are edges of
G (possibly either v = v 0 or x = x0 ). Since |E(G[Ai , Li ])| ≥ 2, we can find such vertices.
So c(vx) 6= c(v 0 x0 ) and {c(vx), c(v 0 x0 )} ∩ c(L) = ∅. We may assume that x, x0 ∈ L \ Tsk+t
by Claim 2.1. Since c(Ai , Tt ) 6= λ(Li ), c(vx) = c(v 0 x0 ) = c(Ai , Tt ), otherwise there is a
rainbow Ck induced by {v} ∪ xLxq or {v 0 } ∪ x0 Lxq by Claim 0.3, see Figure 2.8. Then it
contradicts the fact that c(vx) 6= c(v 0 x0 ).
We have that for any i such that G[Ai , Li ] ⊆ G00j , c(Ai , Tt ) = λ(Li ). This implies that
K[Ai ∪Li , Tt ] is monochromatic of color λ(Li ). Since G00j is connected and Ai s are disjoint,
we have that for any i, i0 such that G[Ai , Li ], G[Ai0 , Li0 ] ⊆ G00j , Li ∩ Li0 6= ∅, so λ(Li ) =
λ(Li0 ) = λ, for some λ. Therefore K[V (G00j ), Tt ] is monochromatic of color λ.
Claim 2.3 For 1 ≤ i ≤ p1 and 1 ≤ j ≤ p2 , 1 ≤ |V (G0i )| ≤ s − 1 and 1 ≤ |V (G00j )| ≤ s − 1.
This claim now follows from the previous instantly.
The following claim deals with a small quadratic optimization problem we shall need.
Claim 2.4 Let n, s ∈ N. Suppose n is sufficiently large and s ≥ 2. Let ξ1 , . . . , ξm ∈ N,
P
1 ≤ ξi ≤ s − 1 and m
i=1 ξi ≤ n. Then
m s − 4
X
1 ξi − 1
+
.
≤n
2
s−1
2
i=1
The equality holds if and only if m =
n
s−1
and ξ1 = · · · = ξm = s − 1.
We use induction on m. If m = 1, then
s − 4
(ξ − 1)(ξ − 2)
(s − 2)(s − 3)
1 ≤
≤n
+
, for any n ≥ s − 1,
2
2
2
s−1
72
where the first inequality becomes equality iff ξ = s − 1, and the second does iff n = s − 1.
Pm−1
P
Suppose m ≥ 2, m
i=1 ξi ≤ n − ξm ,
i=1 ξi ≤ n, and 1 ≤ ξi ≤ s − 1 for 1 ≤ i ≤ m. Since
by induction,
m−1
X
i=1
ξi − 1
2
s − 4
1 +
, for any n ≥ (m − 1)(s − 1) + ξm ,
≤ (n − ξm )
2
s−1
m
where the equality holds iff m − 1 = n−ξ
s−1 and ξ1 = · · · = ξm−1 = s − 1. Hence it is enough
ξm −1
s−4
s−4
1
1
1
to show that (n − ξm ) s−4
+
+
≤
n
+
or
equivalently
ξ
+
m
2
s−1
2
2
s−1
2
s−1 −
ξm −1
≥ 0, and the equality holds iff ξm = s − 1. If ξm = 1, that is obvious. Assume ξm > 1,
2
then
1 ξm − 1
(s − 2)(s − 3) (ξm − 1)(ξm − 2)
ξm
+
−
= ξm
−
2
s−1
2
2(s − 1)
2
2
1
2
1
2
−ξm
+ s−1+
ξm − 2 =
− ξm +
ξm − (s − 1) ≥ 0,
=
2
s−1
2
s−1
s − 4
since 2 ≤ ξm ≤ s − 1.
Claim 2.5 |c(A) \ c0 | + |c(A, L) \ c(L)| + |c(L)| = |E(G)| + |c(L)| ≤ n( s−2
2 +
1
s−1 ).
We have that
p1
p2
X
X
|E(G)| ≤ |E(G1 )| + p1 + |E(G2 )| =
|E(G0i )| + p1 +
|E(G00i )|.
i=1
i=1
Moreover each component G00i of G2 contributes at most 1 to |c(L)| by Claim 2.2, and G1
and G2 are vertex disjoint. So
|c(L)| ≤ n − |V (G1 )| − |V (G2 )| + p2 = n −
p1
X
i=1
|V
(G0i )|
−
p2
X
i=1
|V (G00i )| + p2
73
Hence we have
|c(A) \ c0 | + |c(A, L) \ c(L)| + |c(L)| = |E(G)| + |c(L)|
≤
=
p1
X
i=1
p1
X
|E(G0i )| + p1 +
=
|E(G00i )| + n −
i=1
|E(G0i )| +
i=1
≤
p2
X
p2
X
i
+
|V (G0i )| −
i=1
p1
X
i=1
p1 X
|V (G0 )|
|V (G0i )| − 1 −
i=1
p2 X
|V (G00 )|
i
2
p2
X
|V (G00i )| + p2
i=1
p
2
X
|V (G00i )| − 1 + n
i=1
−
p1
X
|V
(G0i )|
2
i=1
X
p2 00
0
|V (Gi )| − 1
|V (Gi )| − 1
+n
+
2
2
i=1
p1 X
i=1
|E(G00i )| −
p1
X
i=1
−1 −
p2
X
|V (G00i )| − 1 + n
i=1
i=1
For 1 ≤ i ≤ p1 + p2 , let
ξi =
Then
Pp1 +p2
i=1


 |V (G0 )|,
i
if 1 ≤ i ≤ p1
.

 |V (G00 )|, if p1 + 1 ≤ i ≤ p1 + p2
i−p1
ξi ≤ n and 1 ≤ ξi ≤ s − 1 for 1 ≤ i ≤ p1 + p2 by Claim 2.3.
From Claim 2.4, we get
|c(A) \ c0 | + |c(A, L) \ c(L)| + |c(L)|
pX
1 +p2 ξi − 1
s−2
1
≤
+n≤n
+
.
2
2
s−1
i=1
This concludes Part 2 of the proof.
Combining Parts 1 and 2, we see that the total number of colors is at most
c(B) ∪ c(B, A) \ c0 + |c(B, L) \ c(L)| + |c(A) \ c0 | + |c(A, L) \ c(L)| + |c(L)|
ER(s + t, 3sk + t + 1, k)
s−2
1
<
+ (2sk + t)ER(s + t, 2sk + t + 1, k) + n
+
2
2
s−1
s−2
1
≤ g +n
+
,
2
s−1
where g = g(s, t, k) = ER2 s + t, 3sk + t + 1, k .
74
2.4
More precise results for C4
For a coloring c of E(Kn ) and a vertex v, let Nc (v) be the set of colors between v and
V (Kn ) \ {v}, not used on edges spanned by V (Kn ) \ {v}. Let nc (v) = |Nc (v)|. Note that
c(uv) ∈ Nc (u) ∩ Nc (v) if and only if the color c(uv) is used only on the edge uv in the coloring
c. We call this color a unique color in c. For a path P = v1 v2 · · · vk , we say that the path P is
good if c(vi vi+1 ) ∈ Nc (vi ) for i = 1, . . . , k − 1.
Lemma 2.4.1. Let c be an edge-coloring of Kn with no rainbow Ck . If for all v ∈ V (Kn ),
nc (v) ≥ k − 2, then (k − 1) | n and c is k-anticyclic.
Proof. Let c be an edge-coloring of Kn with no rainbow Ck . Suppose for all v ∈ V (Kn ),
nc (v) ≥ k − 2. Then for any v ∈ V , we can find a good path of length k − 2 starting at v by a
greedy algorithm. Let this path be v1 v2 · · · vk−1 , and let c(vi vi+1 ) = i for i = 1, . . . , k − 2. Let
V0 = {v1 , . . . , vk−1 }.
Claim 1 For any u ∈ V \ V0 , c(uv1 ) = 1 or c(uv1 ) 6∈ Nc (v1 ).
Assume that c(uv1 ) ∈ Nc (v1 ). If c(uv1 ) 6= 1 then c(uvk−1 ) must be the same as c(uv1 ),
otherwise v1 · · · vk−1 uv1 is a rainbow Ck . Thus, if c(uv1 ) 6= 1 then c(uv1 ) 6∈ Nc (v1 ).
Claim 2 {c(v1 vi ) | i = 2, . . . , k −1} is a set of distinct colors from Nc (v1 ) and nc (v1 ) = k −2.
From Claim 1 we see that the colors from Nc (v1 ) not equal to 1 appear only on edges v1 vi
for i = 2, . . . , k − 1. Since nc (v1 ) ≥ k − 2, all these edges have distinct colors from Nc (v1 ) and
nc (v1 ) = k − 2.
Claim 3 For any u ∈ V \ V0 , c(uvk−1 ) 6∈ Nc (vk−1 ).
Assume otherwise, then v2 v3 · · · vk−1 u is a good path. Then v1 v3 v4 · · · vk−1 uv2 v1 is a rainbow Ck from Claim 2.
Claim 4 {c(vi vk−1 ) | i = 1, . . . , k−2} is a set of distinct colors from Nc (vk−1 ) and nc (vk−1 ) =
k − 2.
75
By Claim 3, we see that all edges of colors from Nc (vk−1 ) must occur on edges from
{vi vk−1 : i = 1, . . . , k − 2}. Since nc (vk−1 ) ≥ k − 2, edges vi vk−1 , i = 1, . . . , k − 2 have
distinct colors from Nc (vk−1 ) and nc (vk−1 ) = k − 2.
Claim 5 V0 induces a rainbow complete subgraph with all colors unique in c. Moreover,
for each vi and each u 6∈ V0 , c(uvi ) is not unique in c.
This follows from the above claims since for i = 1, . . . , k − 1, vi vi+1 · · · vk−1 v1 v2 · · · vi−1 is a
good path, and nc (vi ) = k − 2.
Consider u 6∈ V0 and a good path of length k − 2 starting at u. Let the vertex set of this
path be V1 . If V0 and V1 share a vertex, say vi , then vi u has a unique color, a contradiction to
Claim 5. Thus the graph is vertex-partitioned into copies of Kk−1 each rainbow colored with
unique colors. To avoid a rainbow Ck , any edges between two fixed parts must have the same
color. Therefore (k − 1) | n and c is k-anticyclic.
By induction on n and the above lemma with k = 4, we have the following results.
Corollary 2.4.2. AR(n, C4 ) = |c∗ | = 4/3n + O(1).
Proof. We need to show that for any edge-coloring c of Kn with no rainbow C4 , |c| ≤ |c∗ | =
4/3n + O(1).
We use induction on n. The statement trivially holds for n = 3. Let c be a coloring of
E[Kn ] with no rainbow C4 , n ≥ 4. If for all v ∈ V (Kn ), nc (v) ≥ 2, then by Lemma 2.4.1, c is
4-anticyclic. So |c| ≤ |c∗ |. Suppose there is a v ∈ V (Kn ) with nc (v) ≤ 1. Let G = Kn − v. Let
c0 be the coloring of E(G) induced by c. Then by induction hypothesis, |c0 | ≤ 4/3(n−1)+O(1).
Hence |c| ≤ |c0 | + 1 ≤ 4/3n + O(1).
Theorem 2.4.3. Let n ≥ 3. Let G be a graph whose edges do not induce a star. Let s = s(G)
and t = t(G) if G is bipartite.
76
maxR(n; G, C4 ) =



 4 n + O(1),
3


n,
if χ(G) = 2 and s(G) ≥ 4 or χ(G) ≥ 3
otherwise
Proof. Suppose χ(G) = 2 and s(G) ≥ 4 or χ(G) ≥ 3 . For the lower bound, consider the
4-anticyclic coloring c∗ . Each color class of c∗ is either K1,m , K2,m , or K3,m for some m ≥ 1,
thus c∗ contains no monochromatic copy of G. The upper bound follows from Corollary 2.4.2.
Suppose G is bipartite and s(G) ≤ 3. We use induction on n. The statement trivially holds
for n = 3. Let c be a coloring of E(Kn ) with no monochromatic G and no rainbow C4 . If
nc (v) ≥ 2 for all v ∈ V , by Lemma 2.4.1 there is a color class of c that induces a K3,3m for
some m ≥ 1, which contains G. Hence we can find a v ∈ V with nc (v) ≤ 1. Then by the
induction hypothesis, maxR(n; G, C4 ) ≤ n. The lower bound is obtained from the coloring c∗∗
with s = s(G) and k = 4. Each color class of c∗∗ is K1,m if s(G) = 2, either K1,m or K2,m if
s(G) = 3 for some m ≥ 1, thus c∗∗ contains no monochromatic copy of G. The total number
of colors in either cases is n.
Acknowledgments The authors thank the referee for a very careful reading and useful
comments improving the presentation of the results.
77
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79
CHAPTER 3.
A short proof of anti-Ramsey number for cycles
Jihyeok Choi
Abstract
This note contains a simplified proof of Anti-Ramsey theorem for cycles by J. J. MontellanoBallesteros, V. Neumann-Lara [5], which was originally conjectured by P. Erdős, M. Simonovits,
V. T. Sós [3].
3.1
Introduction
For a graph H, the classical anti-Ramsey number AR(n, H) is the maximum number of
colors in a coloring of edges of Kn with no rainbow copy of H. It was introduced by Erdős,
Simonovits and Sós [3]. When H is a cycle of length k, Ck , they provided a rainbow Ck -free
1
coloring of edges of Kn with n k−2
2 + k−1 + O(1) colors and conjectured that this is optimal.
They proved the conjecture for k = 3. Alon [1] proved the conjecture for k = 4 and derived
an upper bound for general k. In [4], Jiang and West improved the general upper bound and
mentioned that the conjecture has been proven for k ≤ 7, see also Schiermeyer [7]. Finally,
Montellano-Ballesteros and Neumann-Lara [5] proved the conjecture completely. The main
technique used in [5] is a careful, detailed analysis of a graph representing the coloring, in
particular, proving that each component of such a graph is Hamiltonian if each vertex has
enough “new” colors. This paper uses the same idea as in [5], but shortens the proof.
Theorem 3.1.1. For k ≥ 3 and n ∈ N,
AR(n, Ck ) ≤ n
k−2
1
+
2
k−1
− 1.
80
3.2
Definitions and proofs of main results
Let K = Kn for a fixed n. For an edge coloring c of K, and a vertex v ∈ V (K), let the set of
new colors at v, N EWc (v), be the set of colors used on edges between v and V (K)\{v}, but not
used on edges spanned by V (K) \ {v}. Let newc (v) = |N EWc (v)|. Then the number of colors
used by c on K, |c|, equals newc (v) + |c(K − v)|, where for a subgraph H of K, |c(H)| denotes
the number of colors used by c on the edges of H. Here we simply have written |c| instead of
|c(K)|. For pairwise disjoint subsets X, Y, Z of V (K), let K[X] be the subgraph induced by
X, K[X, Y ] the bipartite subgraph induced by X and Y , K[X, Y, Z] the tripartite subgraph
induced by X, Y , and Z. Then the corresponding sets of colors used in those subgraphs are
denoted by c(X), c(X, Y ), and c(X, Y, Z) respectively. For a subgraph H of a graph G and a
vertex v of G, let degH (v) := |NG (v) ∩ V (H)|.
We now state a version of the Dirac and Ore’s theorems for Hamiltonian cycle which is
essential for our proofs.
Theorem 3.2.1 (Dirac[2], Ore[6]). Let P = v1 , v2 , . . . , vm , m ≥ 3, be a path in a connected
graph G. Suppose degP (v1 ) + degP (vm ) ≥ m.
(i) Then V (P ) contains a cycle of length m in G.
(ii) If P is a longest path in G, then G is Hamiltonian.
We define a few special edge colorings of a complete graph with no rainbow Ck . We say that
an edge-coloring c of K is weak k-anticyclic if there is a partition of V (K) into sets V1 , . . . , Vt
with 1 ≤ |Vi | ≤ k − 1, i = 1, . . . , t, such that (i) for any i, j with 1 ≤ i < j ≤ t, |c(Vi , Vj )| = 1;
(ii) for any i, j, ` with 1 ≤ i < j < ` ≤ t, |c(Vi , Vj , V` )| ≤ 2; and (iii) c has no rainbow Ck .
In addition, if all but at most one of the parts of the partition are exactly of size k − 1 and
the edges spanned by each Vi have own colors (i.e., colors used only once), then c is called
k-anticyclic. We denote a fixed coloring from the set of k-anticyclic colorings of Kn such that
the color of any edge between Vi and Vj is min{i, j}, by c∗ . Then we easily see the following.
81
Lemma 3.2.2. If c is weak k-anticyclic, then
∗
|c| ≤ |c | ≤ n
1
k−2
+
2
k−1
− 1.
Next lemma is the main tool for the proof of the main theorem. It appears in a different
form in [5, Lemma 9]. We include it here for completeness.
Lemma 3.2.3. Let k ≥ 4. Let c be an edge-coloring of K with no rainbow Ck . If for all
x, y ∈ V (K) with x 6= y,
newc (x) ≥ 2 and newc (x) + newc (y) ≥ k − 1,
(3.1)
then c is weak k-anticyclic.
Proof. Consider a representing graph G of c such that it spans K and has exactly one edge
of each color from {N EWc (v) | v ∈ V (K)}. The hypothesis (3.1) gives a bound on degrees of
vertices in G, namely the sum of degrees of two distinct vertices in G is at least k − 1. In the
following, H denotes a connected component of G.
Claim 1 If there is a cycle of length k − 1 in H, then |V (H)| = k − 1.
Suppose not, i.e., there is a cycle, (v1 , . . . , vk−1 , v1 ), and V (H) \ {v1 , . . . , vk−1 } 6= ∅. Since
H is connected, some u ∈ V (H) \ {v1 , . . . , vk−1 } is adjacent to some vertex in {v1 , . . . , vk−1 },
say v1 . If c(u, v1 ) ∈ N EWc (v1 ), then c(u, v2 ) = c(v2 , v3 ); otherwise (v1 , u, v2 , v3 , . . . , vk−1 , v1 )
in K is a rainbow Ck . Similarly c(u, v3 ) = c(v3 , v4 ), . . . , c(u, vk−1 ) = c(vk−1 , v1 ), and eventually
c(u, v1 ) = c(v1 , v2 ), which contradicts that uv1 and v1 v2 are edges of H. Hence c(u, v1 ) ∈
N EWc (u).
By the similar argument as above, we have c(u, {v1 , . . . , vk−1 }) = c(u, v1 ) ∈
N EWc (u). Since we assumed newc (u) ≥ 2, there is w ∈ V (H) \ {v1 , . . . , vk−1 , u} with c(u, w) ∈
N EWc (u) and c(u, w) 6= c(u, v1 ). Considering cycles of length k in K, (vk−2 , u, w, v1 , v2 , . . . , vk−2 )
and (v1 , w, u, v3 , . . . , vk−1 , v1 ), we have c(w, v1 ) = c(v1 , v2 ) = c(vk−1 , v1 ), which contradicts that
v1 v2 and vk−1 v1 are edges of H.
Claim 2
k+1
2
≤ |V (H)| ≤ k − 1 (Hence H is Hamiltonian from by (3.1) and Theorem 3.2.1 ).
82
The lower bound follows from (3.1). If in H every path has at most k − 1 vertices or there
is a cycle of length k − 1, then from Theorem 3.2.1 and Claim 1, we have that the upper
bound holds. Hence we may assume that in H there is a path on at least k vertices, but no
Ck−1 . In particular, we can find a path, v1 , . . . , vk satisfying c(vk−1 , vk ) ∈ N EWc (vk−1 ) since
(i) considering P1 := v1 , . . . , vk−1 , to avoid Ck−1 , we have degP1 (v1 ) + degP1 (vk−1 ) < k − 1; (ii)
from (3.1), without loss of generality we can find a vk ∈ V (H) \ V (P1 ) such that vk−1 vk is an
edge of H and c(vk−1 , vk ) ∈ N EWc (vk−1 ).
Let P2 := v2 , . . . , vk . Then
degP2 (v2 ) ≥ newc (v2 ),
(3.2)
since otherwise there is x ∈ V (H) \ V (P2 ) such that c(x, v2 ) ∈ N EWc (v2 ) and c(x, v2 ) 6=
c(v2 , v3 ), in which case we obtain a rainbow Ck in K, namely (x, v2 , . . . , vk , x). Also we have
degP2 (vk ) < newc (vk ) since otherwise together with (3.2), V (P2 ) induces a cycle of length k − 1
in H by Theorem 3.2.1. Therefore we can find a vk+1 ∈ V (H) \ V (P2 ) such that vk vk+1 is an
edge of H and c(vk , vk+1 ) ∈ N EWc (vk ). Note that vk+1 6= v2 since otherwise (v2 , . . . , vk , v2 ) is
a rainbow Ck−1 in H.
Let P3 := v3 , . . . , vk , vk+1 . Then
degP3 (v3 ) ≥ newc (v3 ),
(3.3)
since otherwise there is y ∈ V (H) \ V (P3 ) such that c(y, v3 ) ∈ N EWc (v3 ) and c(y, v3 ) 6=
c(v3 , v4 ), so (y, v3 , . . . , vk+1 , y) is a rainbow Ck in K.
Now we note that c(v2 , vk+1 ) = c(v2 , v3 ) to avoid a rainbow Ck induced by {v2 , . . . , vk+1 }
in K. Let S = {i + 1 | v2 vi ∈ E(H), i = 3, . . . , k − 1} and T = {j | v3 vj ∈ E(H), j = 4, . . . , k}.
So S, T ⊆ {4, . . . , k} and |S| + |T | ≥ newc (v2 ) + newc (v3 ) ≥ k − 1. Thus |S ∩ T | ≥ 2. Let
i + 1 ∈ S ∩ T where i 6= 3. Then (v2 , vi , vi−1 , . . . , v3 , vi+1 , vi+2 , . . . , vk+1 , v2 ) is a rainbow Ck
(see Figure 3.1 ).
Claim 3 For any two components H and H 0 of G, |c(H, H 0 )| = 1.
83
𝑣1
𝑣2
Figure 3.1
𝑣3
𝑣𝑖
𝑣𝑖+1
𝑣𝑘−1
𝑣𝑘
𝑣𝑘+1
A rainbow cycle (v2 , vi , vi−1 , . . . , v3 , vi+1 , vi+2 , . . . , vk+1 , v2 ).
If there is an edge e between H and H 0 , incident to, say, some v ∈ H of color from N EWc (v),
then we can make H and H 0 connected by adding the edge e and deleting some edge incident
to v of the same color as e in H, so the resulting graph G̃ has a connected component of order
≥ 2( k+1
2 ), which contradicts that every connected component is of order ≤ k − 1. Hence the
colors of edges between H and H 0 are not from c(H) nor from c(H 0 ). Since each component is
Hamiltonian and of order ≥
k+1
2 ,
to avoid a rainbow Ck , by the same type of argument as in
Claim 1, we must have that |c(H, H 0 )| = 1.
Claim 4 For any components H, H 0 , and H 00 of G, |c(H, H 0 , H 00 )| ≤ 2.
By Claim 3, |c(H, H 0 )| = |c(H 0 , H 00 )| = |c(H 00 , H)| = 1. If |c(H, H 0 , H 00 )| = 3, then we can
easily find a rainbow Ck since each component is Hamiltonian and of order ≥
k+1
2 .
Proof of Theorem 3.1.1. When k = 3, it is proved that AR(n, C3 ) = n − 1 in [3]. Suppose
k ≥ 4. We use an induction on n. If n = k − 1, then trivial. If there is v ∈ V (K) with
newc (v) ≤
k
2
− 1, then by induction,
k
k−2
1
k
|c| ≤
− 1 + |c(K − v)| ≤ − 1 + (n − 1)
+
−1
2
2
2
k−1
k−2
1
k
k−2
1
k−2
1
= n
+
+ −2−
−
<n
+
−1
2
k−1
2
2
k−1
2
k−1
If, for any v ∈ V (K), newc (v) ≤
k
2
− 1, then it now follows from Lemma 3.2.2 and 3.2.3.
84
Bibliography
[1] N. Alon, On a conjecture of Erdős, Simonovits, and Sós concerning anti-Ramsey theorems,
J. Graph Theory 1, (1983), 91–94.
[2] G.A. Dirac, Some theorems on abstract graphs, Proc. Lond. Math. Soc. 2 (1952), 69–81.
[3] P. Erdős, M. Simonovits, V. T. Sós, Anti-Ramsey theorems, Infinite and finite sets (Colloq.,
Keszthely, 1973; dedicated to P. Erdős on his 60th birthday), Vol. II, pp. 633–643. Colloq.
Math. Soc. Janos Bolyai, Vol. 10, North-Holland, Amsterdam, 1975.
[4] T. Jiang, D. West, On the Erdős-Simonovits-Sós Conjecture on the anti-Ramsey number
of a cycle, Combin. Probab. Comput. 12, (2003), 585–598.
[5] J. J. Montellano-Ballesteros, V. Neumann-Lara, An anti-Ramsey theorem on cycles,
Graphs Combin. 21 (2005), no. 3, 343–354.
[6] O. Ore, A Note on Hamiltonian Circuits, American Mathematical Monthly 67, (1960), 55.
[7] I. Schiermeyer, Rainbow 5- and 6-cycles: A proof of the conjecture of Erdős, Simonovits
and Sós, Preprint, TU Bergakademie Freiberg, (2001).
85
CHAPTER 4.
General Conclusions
In Part I, a random graph model associated with scale-free networks is studied. In particular, preferential attachment models with the time-dependent selection scheme are considered,
and an infinite dimensional large deviations bound for the sample path evolution of the empirical degree distribution is found.
In Part II, some (edge) colorings of graphs in Ramsey and anti-Ramsey theories are studied.
For two graphs, G and H, an edge-coloring of a complete graph, Kn , is called (G, H)-good if
there is no monochromatic subgraph isomorphic to G and no rainbow (totally muticolored)
subgraph isomorphic to H in this coloring. The set of the number of colors used by (G, H)good colorings of Kn , called a spectrum and denoted by S(n; G, H), is considered. Then the
maximum element in this set, when H is a cycle, is investigated.
4.1
Future improvements and other directions
We plan to continue studying preferential attachment graphs and colorings of graphs:
• In the study of a random graph model (Part I, Chapter 1), we deal with the timeinhomogeneity of the form j/n, which is in an array structure. The method used there
is not applicable directly to other time-inhomogeneous types. For example, if β ≡ 0 and
p(j/n) = 1/j in our model, then it is the Chinese restaurant process, where, with an
infinite number of circular tables, each with infinite capacity, at each time j ≤ n, a new
customer chooses uniformly at random to sit at one of the following j places: directly to
the left of one of the j − 1 customers already sitting at an occupied table, or at a new,
unoccupied circular table. Since this process is already in the ‘boundary’ in our model,
LDP obtained gives only a trivial distribution. It would be of interest to understand nice
86
properties of other time-inhomogeneous models.
• In Part II, Chapter 1, we study the fundamental question of whether the spectrum,
S(n; G, H), is an interval for given n, G and H, i.e. whether, for any k with min S(n; G, H) ≤
k ≤ max S(n; G, H), there is a (G, H)-good coloring of Kn using k colors. It is still open
whether there are graphs G and H such that, for any natural number N , there exists
n > N so that S(n; G, H) is not an interval. This is one of future research goals.
• The maximum element in a spectrum, max S(n; G, H), has already been determined
asymptotically when H is not vertex-partitioned into at most two induced forests. In this
context, max S(n; G, H) is studied when H is a cycle in Part II, Chapter 2. ‘One of the
most intriguing open problems’ in this area is to determine max S(n; K4 , K4 ).

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